Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We research in Saskatchewan, in the Dry Land elucidated by Trevor Herriot (2001). We start with his notion that `those of us who live west of the 100th meridian have been using nostalgia to construct a romance fiction out of our history… there is no denying the power of nostalgia' (p. 2). Our landscape shapes us as we in turn shape it, we feel obligated to be sensitive to the glamours of nostalgia. Our prairie landscape evokes questions: What does it mean to research ethically in this space? What are the possible foci of our nostalgia? And, how do we navigate the ethical paradoxes in which we find ourselves? This article argues that research pushed by cultural norms creates double-binds around notions of `good' and `R/right'. By juxtaposing nostalgia and goodness, we examine ethical paradoxes facing us as academics in renegotiating these double-binds.
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.
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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it