Bibliographic record
Abstract
The human pointing gesture may be viewed from many angles. On a basic description, it is an intentional movement, often of the hand, by which one person tries to direct another’s attention toward something; it is, in short, a bodily command to look. But this definition is only a start. Pointing may also be seen as a semiotic primitive, a philosophical puzzle, a communicative workhorse, a protean universal, a social tool, a widespread taboo, a partner of language, a part of language, a fixture of art, a graphical icon, a cognitive prop, a developmental milestone, a diagnostic window, a cross-species litmus test, and an evolutionary stepping-stone. A tour of these fifteen ways of looking at pointing reveals the diverse dimensions of one of our most unassuming, ubiquitous behaviors. It also reveals a series of dualities that make the gesture especially compelling: it is at once natural and irreducibly cultural; simple yet put to sophisticated purposes; by turns salient and subtle; and is—in its prototypical form, with the index finger extended—special in some ways and not so special in others. These tensions in part explain why pointing has been treated so widely and variously across disciplines. But there is also, I propose, a deeper reason: The gesture embodies our distinctively human preoccupation with attention.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".