Bibliographic record
Abstract
Research studies and experience suggest that many students just do not understand citation and referencing. They say they know the rules, they seem to know the rules, yet still they make mistakes, sometimes with heavy consequences. For those who do understand, there is no problem. For those who understand what is expected by way of good practice, the main difficulty may be understanding the understandings of those who do not understand, those who do not mean to cheat but who still break "the rules." In this paper, I investigate sources of confusion, and possible disconnects between those who teach citation and referencing and those who learn and use these techniques. The study includes a series of surveys of librarians, teachers and students. Strategies and techniques to promote better understanding and better practice are suggested. Teacher-librarians are well-placed to promote and ensure good practice.
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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 | 0.052 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.044 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".