jsort

sort, but the key is a description.

$ jsort -o "more hawkish about inflation" presser.txt | head -6
2.18	0.12	The plain fact is that inflation is too high and has been for too long.
2.18	0.16	I defined the standard for action: We must be confident that underlying inflation is moving to our objective, clearly and at sufficient speed.
1.76	0.19	Yet for more than five years, inflation has been running above target.
1.68	0.36	Too many categories are still posting increases above 3 percent, on both a 6- and 12-month basis.
1.52	0.38	In the meeting just concluded, the FOMC decided to raise the target range for the federal funds rate by ¼ percentage point to 3¾ to 4 percent, in support of the Federal Reserve’s dual mandate.
1.39	0.22	The committee’s unanimous vote shows our resolve to achieve price stability on a timelier basis.
jsort: 51 texts, 255 comparisons in 10 rounds; reliability 0.96; first-position lean -0.07; 255 calls, 0 cached; 87,187 tokens; $0.0037; 7.2s

$ jsort -r "more hawkish about inflation" presser.txt | head -3      # the other end, from the cache
And with that, I’ll take a few of your questions.
New hiring, private-sector earnings, business capital investment—each of these markers has improved in recent months and is pointing in a good direction.
Those who are least well-off have the most to gain from a durable expansion, a solid labor market, and stable prices.

presser.txt is the chair's opening statement at the FOMC press conference of September 16, 2026, one sentence per line. The first column is the score and the second its standard error.

jsort shows Jev, TypeSafe's decision model, two texts at a time and asks which ranks higher on the dimension you described. Jev answers with a probability in about 200 ms. jsort asks about a few pairs per text, not all of them, fits a Bradley-Terry scale to the answers and prints the texts from the top of the scale down. With -o each line carries its score and a standard error, so the order can be used as a measurement and not only as a ranking.

The same command sorts whole documents. Every opening statement since the first FOMC press conference, 95 of them from April 2011 to this week, as files named by date:

$ jsort --whole -o --max-chars 16000 "more hawkish about inflation" statements/*.txt | sed -n '1,4p;92,95p'
6.91	0.35	statements/2022-11-02.txt
6.27	0.49	statements/2022-09-21.txt
6.06	0.53	statements/2022-07-27.txt
5.64	0.41	statements/2022-06-15.txt
-3.01	0.23	statements/2020-11-05.txt
-3.05	0.22	statements/2020-04-29.txt
-3.10	0.22	statements/2020-07-29.txt
-3.46	0.17	statements/2020-06-10.txt
jsort: 95 texts, 475 comparisons in 10 rounds; reliability 0.98; first-position lean -0.04; 0 calls, 475 cached; 0.1s

That run came from the cache. The first time it was under half a minute and about seven cents, for statements of about 1,250 words each. The top four are 2022 hikes of 75 points. The bottom is the first year of the pandemic. This week's statement, a 25-point hike, ranks 15th of the 95. By chair, the average score is -2.19 for Bernanke (12 statements), -1.29 for Yellen (16), 0.62 for Powell (64) and 2.32 for Warsh (3). bench/fed.py builds both files from federalreserve.gov and checks the scale against what the Committee did; the results are below.

A thousand short lines take about 5,000 comparisons: roughly a minute and seven cents.

Install

uv tool install jev-sort        # the command it installs is jsort

jsort finds a key the way jgrep does: TYPESAFE_API_KEY, OPENROUTER_API_KEY, or a System One gateway (JEV_GATEWAY_URL and JEV_GATEWAY_API_KEY), from the environment or from ~/.config/jev/typesafe.key, openrouter.key or gateway.key. Force a choice with --api or JEV_API. The two tools share one answer cache.

Use

jsort "more urgent" tickets.txt | head                       # the ten most urgent
jsort -r "more urgent" tickets.txt | head                    # the ten least
jsort -o "more hawkish about inflation" statements.txt       # with scores and standard errors
jsort --top 5 "a more serious safety problem" complaints.txt
jsort --para "makes a stronger causal claim" paper.txt       # paragraphs, not lines
jsort --whole "of more general interest" abstracts/*.txt     # whole files; prints their names
jsort --jsonl --field event.message "angrier" events.jsonl   # full records come back, sorted
jsort --csv --field narrative -o --keep-order --name breadth \
      "drew broader participation" events.csv > scored.csv    # add a variable, keep the row order
jsort --json "more urgent" tickets.txt                       # rank, score, se and source line

Blank lines are dropped. Identical texts are compared once and share a score. Several files are sorted together, as sort does. Exit status is 0 when the input was sorted and 2 on any error: an unreadable file, a failed comparison, a stop at the budget, the API refusing further calls because the credit ran out. In each of those cases whatever could be sorted from the answers already paid for is still printed.

CSV needs a header with unique column names. A byte-order mark is ignored, a row with more values than the header keeps them at its end, and a header with no rows comes back as a header. With -o, jsort refuses to write over a column or JSON field that already exists; pick another prefix with --name.

Write the description as a comparative: "more urgent", "easier to read", "drew broader military participation". It goes into one question, Text A ranks higher than text B on this criterion: "...", so anything that completes that sentence sensibly will work.

What the numbers mean

Score. The position on the scale, in logit units, centred on zero. A gap of 1.0 between two texts means Jev gives the higher one about 73% in a head-to-head; a gap of 3 means about 95%. Scores are relative to the other texts in the same run. They do not carry over to another file or another description.

Standard error. How well the comparisons pin the score down. Jev's answer is a probability, not a win or a loss, so the model is a fractional logit and the errors are the robust (sandwich) kind, with a leverage correction because there is one parameter per text. Where Jev's answers line up on one scale the errors are small. Where they contradict each other the errors grow. In simulation, intervals of 1.96 standard errors cover the target 94 to 95% of the time from -k 6 up (bench/simulate.py). Above 4,000 distinct texts the same errors are estimated by random probing, to within about 7% each, because the matrix the exact ones need no longer fits.

Reliability. The comparisons are dealt into two halves, a scale is fitted to each, and the correlation between the two is stepped up to full length (Spearman-Brown). Near 1, the order does not depend on which pairs happened to be asked. Below 0.8 jsort says so: raise -k, or the description is not one these texts can be ranked on. In simulation it tracks the true figure closely (0.955 reported for 0.964 at the default) and errs on the cautious side.

First-position lean. How far the first-shown text's chance sits from a half in an even matchup. Positions are randomised and balanced and the lean is estimated with the scale, as home advantage is in a sports model, so it does not tilt the scores. It has been a few points either way.

From Python

import jsort

r = jsort.rank(statements, "more hawkish about inflation", per_item=10)
for i in r.order()[:5]:
    print(f"{r.score[i]:6.2f} ±{r.se[i]:.2f}  {statements[i]}")
r.reliability, r.lean, r.asked

r.score, r.se and r.comparisons are arrays aligned with the input. The command's seat belt applies here too: spending stops at budget= dollars, by default $JSORT_BUDGET or 1.00, and r.over_budget says whether it was reached. It works inside a notebook. jsort.arank is the same thing as a coroutine, for a client you already hold.

How it chooses pairs

All n(n-1)/2 pairs are never needed. A lopsided pair says almost nothing, because the answer was predictable. A pair of near neighbours says the most. The first round is a random ring: every text meets two others, once in each position, which connects everything. After each round the scale is refitted, and the next round pairs texts that currently sit next to each other, as a Swiss-system tournament does. Each score is jittered in proportion to how uncertain it still is, so a text that is not pinned down yet keeps meeting new neighbours. A pair is asked at most twice, once in each order. With a handful of texts jsort simply runs out of pairs and stops early.

--top N adds one rule. Once a text has three comparisons, jsort asks how much worse the fit would get if that text were moved up to the edge of the top N. If the answer is "much worse", the text gets no more questions. A text that lost 0.03 to 0.97 against a middling opponent is out, and no further comparison will change that. In simulation this is the same cost as a full sort or a little less, and finds more of the true top ten (0.85 against 0.77 of them, among 2,000 texts).

Cost

A comparison bills about 270 tokens of overhead plus both texts and the description. For short lines that is about 320 tokens, or $0.0000135 at $0.042 per million, and the default -k 10 asks five questions per text: about 7 cents per thousand lines. For 170-word excerpts a comparison is about 735 tokens and a thousand texts cost about 15 cents. A run is about ten rounds, each as slow as its slowest call, so a small sort takes five to ten seconds. A large one manages about 90 comparisons a second through OpenRouter.

jsort stops asking at --budget, one dollar by default, and sorts on what it has. Nothing is lost: every answer is cached in ~/.cache/jev/answers.sqlite, and the choice of pairs is seeded, so a rerun with a higher budget, or a higher -k, starts by replaying the same questions from the cache and only pays for the new ones.

How well does it work

Two checks, run on 2026-09-19 with Jev 1.13 through OpenRouter. Both run the installed jsort command, uncached.

Against people making the same comparisons. bench/readability.py. The CommonLit Ease of Readability corpus (Crossley et al. 2022) has 4,724 excerpts written for grades 3 to 12. Each has an easiness score that is itself a Bradley-Terry fit, to teachers' judgments of which of two excerpts is easier. So this is the same method with Jev in the teachers' chair. On a random 300 excerpts, the teachers' scale has a reliability of about 0.78, which means no measure can be expected to correlate with it above about 0.88.

Two things to take from it.

jsort gets most of the way to the ceiling from a two-word description. It beats every readability formula shipped with the corpus by a wide margin, and it beats the other thing you can do with two words, which is to ask for a probability per text and sort on that. That probability bunches up (73 distinct values among 300 texts), and it is weakest exactly where a sort is needed, on texts that are close. On pairs the teachers put between 0.25 and 0.5 apart, jsort agrees with them 67.5% of the time against 63.2%.

The default is already on the plateau. -k 10 and -k 16 give the same answer, -k 6 is nearly there, and the reliability of 0.98 says the same. Five cents' worth of comparisons orders 300 passages at r = 0.82, against a ceiling of 0.88.

Against what the Fed then did. bench/fed.py. The 95 opening statements above, sorted whole on "more hawkish about inflation", against the top of the target range for the federal funds rate (FRED series DFEDTARU). Both tests were written into the script before the sort was run. The rank correlation between a statement's score and the move announced that day is +0.46 (95 statements). With the change in the target over the following 180 days it is +0.37 (91 statements). And the scale picks up what the rate decision alone does not: the holds of June, September and November 2023 all rank among the thirteen most hawkish, because the chair held rates and talked tough. It gets the shape of fifteen years right. The six statements that announced hikes of 50 or 75 points in 2022 are the top six, seven of 2023's eight statements fill ranks 7 to 13, and the bottom eight are all from March 2020 to January 2021.

Tips

• Write the description as a comparative and keep it to one dimension: "more urgent", not "more urgent and more polite". Stance ("more hawkish"), urgency and difficulty ("easier to read") are the dimensions shown above.
• jsort shines where levels are hard to name. You do not need a rubric or labeled examples, only a way to say what "more" means, and it keeps discriminating among texts that sit close together.
• When the scale is going into a model, use -o: every score comes with a standard error, and the run comes with a reliability figure, so the measurement error travels with the measure.
• For results that must reproduce, pin the model with --model (for example typesafe/jev-1.13 on OpenRouter) and keep the cache file with the project. The cache makes a rerun exact and free.
• For long documents raise --max-chars, or cut them to the passages that bear on the dimension first (jgrep --para is one way). Counts and dates are better computed in code.

Development

uv sync && uv run pytest              # offline: a fake API, no key
uv run python bench/simulate.py       # offline: a simulated judge with a known scale
uv run python bench/probe.py          # live, under a cent: which question shape to use
uv run --group bench python bench/readability.py prepare && uv run --group bench python bench/readability.py run

src/jsort/model.py is the scale: the fit, the standard errors, reliability and the test --top uses. schedule.py chooses pairs. engine.py runs the rounds and is the Python API. inputs.py reads lines, paragraphs, files, CSV and JSONL. core.py is jgrep's client, unchanged: backends, retries inside a time budget, the cache, in-flight deduplication and the cost meter.

Why a noul and not a choice: bench/probe.py asks every ordered pair of ten texts both ways. A two-option choice and a noul ("A ranks higher than B") made the same decisions, 95.6% correct, but the choice put 93% of its answers below 0.1 or above 0.9, where the noul left 28% of its answers between the two. Those in-between answers are what a scale is fitted from.

MIT license.