Bibliographic record
Abstract
Island studies can be deceptively difficult for inexperienced undergraduates due to the field’s trans-disciplinary and international scope, advanced academic content and engagement with a wide range of cognitive processes and methodologies. At the same time, island studies can potentially transform and motivate students on a personal level by tapping into their experiential knowledge when they adopt an island-centred standpoint. Such a stance is challenging to measure and not automatically or readily achieved. A teacher of island studies must therefore be sensitive to presenting and studying islands ‘on their own terms’, but realistic as to what progress can be made at an introductory level by general students. This paper draws upon the author’s experience in teaching the core introductory survey course in island studies to undergraduates at the University of Prince Edward Island from 2007 to 2009. That experience is examined in light of the dilemmas which relate to indigenous island geographies.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".