Reflections on Teaching Referencing: What Four Case Studies Can Tell us About Developing Effective Teaching Strategies
Bibliographic record
Abstract
Two contradictions are inherent in our research into referencing practices and the subsequent development of teaching strategies to remedy inappropriate practices. First, aggregate studies and teaching strategies that tend toward a one size fits all formula for researching and teaching referencing do not consider individual differences in students’ development of the complex set of skills that we know are involved in referencing practice. Further, although we say that we want students to be creative in their reading and writing practices, our teaching encourages them to look for correct answers in their reading of sources and to imitate set formulae for writing essays. This article examines four case studies taken from a larger aggregate study of EL1 and EL2 students. In their interviews and essay scripts, these students show varying levels of awareness of appropriate referencing practices. After examining these differences, I adapted Ada’s (Cummins, 1996) framework for comprehensible input and critical literacy, as well as work by Hinkel (2002), Keck (2006), and Kintsch (1998), to develop some strategies for teaching referencing that address individual differences.
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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 | 0.001 | 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 | 0.005 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".