Social Annotation for Power Negotiation
Bibliographic record
Abstract
Social annotation and role-play are two pedagogical approaches that promote active, student-centred learning. In this paper, we report on how the two approaches were combined in a senior-level university course that aimed to reveal the multiple dimensions and complexity of policy development and decision-making for natural resource management. We begin with a review and analysis of social annotation and role-play as teaching strategies. We then describe their combined implementation in the senior-level course—including reflections from the course instructor and a student in the class—while situating our reflections within the context of an existing framework for critical social annotation. We conclude that when implemented together, and with careful preparation and clear expectations of student conduct, the complementary strengths of social annotation and role play offer unique opportunities to subvert hegemonic models of knowledge production and exchange. The addition of students’ role-played annotations enabled us to redefine whose knowledge and experience are worthy of consideration by giving voice to students as authorities alongside authors of texts and by filling in gaps in the perspectives presented in texts.
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 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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".