Bibliographic record
Abstract
In recent years, attention has become a matter of increasing public concern. New digital technologies have transformed human attention materially and discursively, reorganizing perceptual practices and inciting debates about them. The essays in this special issue emerged from a set of panels focused on attention at the 4S conference in New Orleans in 2019. They are all, in various ways, concerned with shifts among attention’s many meanings: between payment and care, instinct and agency, or vulnerability and power. Drawing on Science and Technology Studies (STS) sensibilities, these pieces examine how scientific and technical actors are invested in theorizing and capturing attention, while simultaneously engendering new forms of care, resistance, and critique. At a moment where the attention economy appears to be in transformative crisis, this collection maps a set of incipient directions that ask us to pay attention to not only attention itself but also to the many sociotechnical settings where experts and publics are shifting attention’s meaning and value.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".