Review of “In the Midst of Things: The Social Lives of Objects in the Public Spaces of New York City”
Bibliographic record
Abstract
A decade ago, I was interviewed by Daniel Dale, then urban affairs reporter for the Toronto Star, and now a high profile “fact checker” at CNN. Dale had just witnessed an incident wherein a young woman lunged unsuccessfully at the subway doors and lost control of the coins she was carrying, hurling them onto the floor of the northbound train as it sped away. In his article, “The Free Money Nobody Wanted”, Dale puzzled why none of the riders picked up a toonie (a two dollar coin in Canada) which had fallen into a corner of the subway car. I was reminded of Dale’s article when reading the fourth chapter, “The Subway Door,” of Mike Owen Benediktsson’s terrific new book In the Midst of Things: The Social Lives of Objects in the Public Spaces of New York City. Like Dale, the author divines that there is an emergent social order that materializes on subway trains, especially with regards to passengers obstructing doors that are closing. Brief and superficial as these social interactions may be, they are crucial to the speed and efficiency with which underground transportation functions in New York.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".