Bibliographic record
Abstract
To the Editor: I write in reference to the recent scoping review and accompanying Invited Commentary on the hidden curriculum (HC).1,2 One calls for greater precision in terminology; the other advocates for a conceptual fluidity to ensure that the generative power of HC is retained. I have sympathy for both positions but do not see them as irreconcilable. A generative conceptualization may need to be preserved in the context of exploring novel social situations; however, science also requires a degree of precision and agreement in modeling the world, lest the collective becomes irreconcilably fragmented and strange to itself. So, is there a way to afford greater precision and generative fluidity in the concepts we use? The problem lies in the taxonomic reflex that pervades our field, which asserts that term X means this and only this. The response that term X can mean anything you want it to mean is still caught in this taxonomic discourse. The solution, I would suggest, is the use of pattern language.3 For example, taxonomically a garden pea is the seed of the plant Pisum sativum from the Fabaceae family and so on. The properties of the pea are inherited and understood in the context of its class and phylum. A pattern language, on the other hand, describes the pea in terms of its facets (round, green, small, edible, and so on). These facets are relatively simple and unambiguous constructs that can be recombined to describe a great many different things. While the pea does have a singular genetic lineage (it is not descended from pigeons or princesses), social phenomena such as those modeled by various uses of HC are not. A pattern language approach can be particularly effective in modeling complex social phenomena without resorting to exclusionary taxonomic regulation. In the context of HC, “hidden” is a facet, as are “informal,” “perceived,” and “null.” We might agree on the meaning of the terms of the pattern language we use to describe the plurality of HCs without abandoning either precision or fluidity. Rachel H. Ellaway, PhDProfessor, University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada; ORCID: http://orcid.org/0000-0002-3759-6624; [email protected]
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".