Interdisciplinary applications of human time use with generalized lexicons
Bibliographic record
Abstract
Time use studies quantify what people do, over particular time intervals. The results of these studies have illuminated diverse and important aspects of societies and economies, from populations around the world. Yet, these efforts have advanced in a fragmented manner, using non-standardized descriptions (lexicons) of time use that often require researchers to make arbitrary designations among non-exclusive categories, and are not easily translated between disciplines. Here we propose a new approach, assembling multiple dimensions of time use to construct what we call the human chronome, as a means to provide novel interdisciplinary perspectives on fundamental aspects of human behaviour and experience. The approach is enabled by parallel lexicons, each of which aims for low ambiguity by focusing on a single coherent categorical dimension, and which can then be combined to provide a multi-dimensional characterization. Each lexicon should follow a single, consistent theoretical orientation, ensure exhaustiveness and exclusivity, and minimize ambiguity arising from temporal and social aggregation. As a pragmatic first step towards this goal, we describe the development of the Motivating- Outcome- Oriented General Activity Lexicon (MOOGAL). The MOOGAL is theoretically oriented towards the outcomes of activities, is applicable to any human from hunter-gatherers to modern urbanites, and deliberately focuses on the physical outcomes which motivate the undertaking of activities to reduce ambiguity from social aggregation. We illustrate the utility of the MOOGAL by comparing it with existing economic, sociological and anthropological lexicons, showing that it exhaustively covers the previously-defined activities with low ambiguity, and apply it to time use and economic data from two countries. Our results support the feasibility of using generalized lexicons to incorporate diverse observational constraints on time use, thereby providing a rich interdisciplinary perspective on the human system that is particularly relevant to the current period of rapid social, technological and environmental change.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".