Career Sustainability: Framing the Past to Adapt in the Present for a Sustainable Future
Bibliographic record
Abstract
The emerging literature concerning sustainable careers posits that career development is an adaptive and dynamic process of creating person–career fit, in pursuit of a career that is happy, healthy, and productive. Our goal is to advance this literature by delving deeper into the intrapersonal processes involved in constructing career sustainability—which involves meeting one’s needs in the present without sacrificing one’s needs in the future—and clarifying the role of time in this process. We articulate a fundamentally subjective, intrapersonal process of enacting career sustainability that draws upon career construction theory, prospective and adaptive sensemaking, conservation of resources theory, and career adaptability to articulate how individuals reflect, frame, envision, re-frame, and ultimately, adapt to effect and maintain their career sustainability over time. This expansion brings added conceptual depth to earlier sustainable careers models by situating the career firmly within the agency of the career actor and articulating how this process unfolds with specific recognition of the past, present, and future. Educators, career counselors, HR representatives, and community organizations are called upon to promote and support career sustainability and support individuals through this dynamic and adaptive process.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| 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".