Academic Folk Wisdom: Fact, Fiction and Falderal
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
Each generation of professors and students is heir to the academic folk wisdom of its predecessor. However, empirical evidence calls several tenets of this well-intentioned legacy into question. Specifically, data presented here suggest the following iconoclastic conclusions: placing a few easy questions at the beginning of a multiple-choice examination does not build student confidence; changing the first-chosen answer to a multiple-choice question can frequently be beneficial; printing multiple-choice examinations on paper of different colours (to discourage cheating) can be disadvantageous to students; choosing ‘c’ when in doubt about an answer is not an effective multiple-choice examination strategy; students sitting in front/middle seats do not always receive the highest marks; most students are not academically dishonest; humour on examinations enhances student performance; the highest grades are not achieved in morning classes; most students do not perform considerably better on multiple-choice than essay questions (or vice versa); and, students with unusual names do not typically earn poor grades. Consequently, caution is advisable in the acceptance of apparent academic truisms.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| 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