A real experiment is a factorial experiment?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it