Bibliographic record
Abstract
From its first incarnation as the Working Group on Time Budgets and Social Activities at the International Sociological Association meeting in Varna, Bulgaria in September 1970, the International Association for Time Use Research has had global interests and attracted a global audience. Two of the first twenty conferences took place in Mexico City (Mexico 1982) and Delhi (India 1986). Early IATUR members hailed from Brazil, Colombia, the Dominican Republic, Egypt, Kuwait, India, Mexico, Nigeria, Sri Lanka, South Africa, Tanzania, Thailand, Turkey and Venezuela. Nevertheless, until very recently, most time use surveys were collected in the more developed Northern Hemisphere countries (Fisher et. al. 2011). The overwhelming majority or time-relevant publications and papers presented at academic conferences have concentrated on daily activity patterns in Australia, Canada, the USA, European countries, and the more developed North-East Asian countries. While IATUR Regional Council Members have tended to live and work in the regions they represent, until 1992, the Council Member for Africa was based in Europe or North America. Prior to the election of co-Vice-Presidents Lara Gama de Albuquerque Cavalcanti from Brazil and An Xinli () from China in 2011, no member of the IATUR core executive came from the global south.
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 | 0.039 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.023 |
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".