Bibliographic record
Abstract
Most studies addressing lexical processing make use of factorial designs. For many re-searchers in this field of inquiry, a real experiment is a factorial experiment. Methods such as regression and factor analysis would not allow for hypothesis testing and would not contribute substantially to the advancement of scientific knowledge. Their use would be restricted to exploratory studies at best. This paper is an apology coming to the defense of regression designs for experiments including lexical distributional variables as predictors. In studies of the mental lexicon, we often are dealing with two kinds of predictors, to which I will refer as treatments and covariates. Stimulus-onset asynchrony (soa) is an example of a treatment. If we want to study the effect of a long versus a short soa, it makes sense to choose sensible values, say 200 ms versus 50 ms, and to run experiments with these two settings. If the researcher knows that the effect of soa is linear, and that it can be administered independently of the intrinsic properties of the items, then the optimal design testing for an effect of soa is factorial. One would loose power by using a regression design testing for an effect at a sequence of SOA intervals, say 50, 60, 70,..., 200 ms. This advantage of sampling at the extremes is well-known (see, e.g., Crawley, 2002, p. 67): the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.224 | 0.456 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".