Bibliographic record
Abstract
Attention has been a focus of much of the work I’ve done over the last several years, and in particular the ‘attentional economies’ (Lankshear and Knobel, 2002; de Castell and Jenson, 2005) involving teachers and learners in both classroom-based and non-formal educational environments. What has become very clear is the need to distinguish between attentional behaviours, and other, more complex, more demanding and more generally ‘educative’ kinds of attention. I’m drawn to the idea of ‘exquisite attention’, (Lather 2007:16) and particularly interested in the pleasures of attention, as well of course as its productivity. Maybe its exquisiteness, a pleasure of attention, can be related to its productivity. We have long known that attention can be productive indeed, all by itself, seemingly. Consider the Hawthorne Effect, notorious among researchers---as soon as I begin to pay attention to you, you get better at what you are doing. Having other people pay close and careful attention to what you are doing often helps you do it better. But I want to argue that exquisite attention can never come from other people, but only from ourselves. And I want to set out some ways to see an ethics, an epistemology and a pedagogy in this small claim. Probably many of us gain considerable enjoyment from having other people pay attention to our work, but I’m going to argue that this is a mistake, and a misleading one, in several important ways.
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 | 0.020 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".