Living life ‘to the core’: Enacting a calling through configurations of multiple jobs
Bibliographic record
Abstract
Most of us will be familiar with the saying, ‘Find something you love to do, and you’ll never have to work a day in your life’. But is it accurate? Through interviews with individuals who have felt beckoned towards such an activity – in other words, who have a calling – we explain why this saying holds true for some, but not for others. We found that many called individuals have conditions, which are self-determined limitations on how, where and with whom they are driven to engage in their callings. Drawing on this idea, we differentiate a calling core, comprised of activities that meet all an individual’s conditions, from periphery activities that fall within the domain but only meet some or no conditions. Core conditionality can, in turn, explain the configuration of jobs people will be inclined to pursue in turning their calling into a career. For example, some called individuals with conditional cores deliberately eschew all-encompassing callings, instead pursuing stable non-calling work alongside part-time calling jobs that meet all their conditions. We also learned why individuals may change their enactment approaches over time as they develop a clearer understanding of what conditions truly matter to them.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| 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; 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".