People in Transit: Expanding the Spatial and Temporal Frames of the American Anthropological Association Annual Meetings
Bibliographic record
Abstract
This photo essay comprises a series of black and white photographs taken in transit. The photographs were taken in airports, planes, busses, railway platforms and underground metro stations during the photographer’s journey to the 2017 American Anthropological Association (AAA) Annual Meeting in Washington, DC. Taken in high contrast, the images presented here reflect the photographer’s acute awareness of the sharp conceptual boundaries that separate the scholarly arena located inside of the venue’s walls from the social, political, economic and spatial worlds that exist outside of them. The essay provides a visual narrative of the photographer’s observations of people and places while on route to the event. In doing so, it highlights everyday lived experiences that occurred “just before” the official onset of the event, while “on the way” to the venue. Through careful attention to shadows and viewpoint, the photographer subtly anonymizes subjects, maintaining the fabric of their relationships as passing strangers. While at once a reflexive commentary on the temporal and spatial frames of conferences, the essay also documents the diverse ways that people temporarily dwell in ephemeral travel spaces.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".