Do Self-Assessments Work to Detect Workshop Success?
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
D'Eon et al. concluded that change in performance self-assessment means from before to after a workshop can detect workshop success in their and other situations. In this commentary, their recommendation is refuted by showing that (a) self-assessments with balanced over- and underestimations are still biased and should not be used to evaluate workshops, even though the means of self-assessments and criterion measures are artificially equal; (b) participants' performance should not be attributed directly to training, even if the self-assessments are psychometrically valid and obtained prior to the workshop as well; (c) self-assessment findings should not be generalized to other situations without further analysis and caution, even if the participants' performance can be attributed to training. For clarifying the recommendation by D'Eon et al. to use ``aggregated self-assessments'' to evaluate workshops, analysis of multilevel data is explained and discussed. Finally, nine rules of thumb in using self-assessments for evaluating training are provided.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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