“HOW, WHEN, WHY” – A COMPARISON OF TWO ACTION RESEARCH METHODS TO EXAMINE THE HIDDEN TONES IN ANNOTATION FEEDBACK
Bibliographic record
Abstract
This recent study broadly confirms earlier conclusions in which action research findings identified that annotated feedback on student assignments carried an unfavourable lecturer tone and, because of which, failed to motivate the student as a learner. It was important to take the action research process further to show how tone is so easily manifested in annotation. By subverting the feedback process, annotation was read as marginalia in temporary isolation of the assignment and tone was easily identified. Two different action research (AR) studies were carried out by researchers to examine the same issue. One study examined annotation using participatory action research (PAR) (Marshall et al. 2011), while the other study utilised action research using semi-structured questionnaires (McNiff et al 2003). This paper demonstrates how the chosen methodology can either support or restrict action research if the methods are considered ill-matched to the study. It also demonstrates the importance of triangulation. Therefore, the
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 | 0.180 | 0.219 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".