Ascribing Meaning to Occupation
Bibliographic record
Abstract
Ascribing meaning to occupation is a multifaceted process. Understanding this process is illusive, yet fundamental to theory and practice. The objective is to describe the meaning that mothers ascribe to their occupations. A secondary analysis was conducted with data from a convenience sample of 35 Israeli mothers, ages 25 to 45 years. Data were collected using the Occupational Performance History Interview as part of a larger study. Interviews were transcribed verbatim and content analysis applied. Two main categories emerged: the meaning of "giving" (investing values) and the meaning of "receiving" (ensuring needs are met). Values such as responsibility require mothers to do occupations they find less desirable than others associated with the mothering role. The study illustrates how values and needs are intertwined to contribute to the meaning of occupation. Moreover, meaningful occupations can be undesirable but doing them arises from the values that drive mothers to fulfill this role.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".