Bibliographic record
Abstract
This article investigates the postindustrial temporal landscape, or ‘timescape’, through a case study of a specific industry: Internet advertising. The theoretical portion of this article finds that the expansion of digital information communication technologies (ICTs) has radically transformed time keeping into ‘calculation’ in many of today’s workplaces. Additionally, globalized production has also rendered many locally constructed symbols of time less relevant. The author contrasts these events to the domestic time, which is constructed through contextual events and symbols, thereby making the postindustrial timescape further estranged from the domestic than even the Fordist timescape. The empirical portion of this article summarizes qualitative findings of time reckoning among Internet advertising workers. Time is not constructed out of local, material experiences but through digital means. This estranges domestic time even further, which has unintended but differential gendered effects. The implications of these findings include the emergence of a new sense of precarity, one based on ‘productivity’ of time spent on work. Additionally, a potentially new ‘glass ceiling’ could be emerging, based on the increased levels of home-based paid work. Women’s domestic responsibilities may make it relatively more difficult for them to advance when home-based paid work is expected.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".