Lockdown Stories: A Qualitative Assessment and Comprehensive Taxonomy of Career Resources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Career resources are receiving increasing attention in the context of career development. This paper utilizes M. E. Ford's (1992) ten components of effective functioning to provide a comprehensive typology of factors likely to act as career resources and test this proposition in a context of career shock with a narrative design. In the weeks following the first COVID-19 lockdown, 42 participants were asked to complete a questionnaire about their well-being, perceived employability, and emotional anticipation of their career future, as well as to write three stories about their experience with the lockdown. M. E. Ford's categories were used to identify and code the resources and obstacles mentioned in the stories. Results show the relevance of such a taxonomy to classify both career resources and obstacles. Additionally, the type of story (general story, positive or negative story) in which career resources and obstacles were mentioned played a significant role in their association with the quantitative measures. Conceptual and practical implications are discussed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it