Synchronicity Learning Theory: Happenstance Learning Theory Re-envisioned
Bibliographic record
Abstract
The purpose of this study is twofold: firstly, to listen for elements of Krumboltz’s (2009) Happenstance Learning Theory (HLT) within the stories of six women, including risk, curiosity, optimism, flexibility, and persistence; and secondly, to determine whether these women frame their stories within a worldview that values other ways of knowing, such as intuition. Women have been selected because they are at least fifty years old and have acquired the embodied wisdom that results from years of lived experience. Their stories have potential to contribute women’s voices to inform a new model of career counselling which re-envisions HLT, where an exploration of worldview is considered part of the conversation around meaningful happenstance, called synchronicity. Counsellors may offer this new approach, named Synchronicity Learning Theory (SLT), in order to encourage an awareness of synchronistic experiences that help guide decision making within an interconnected and interdependent world. Using a narrative inquiry design, in-depth interviews were recorded and verbatim transcriptions were woven together in a storied form that includes six main themes that help inform SLT: 1) risk; 2) boundaries; 3) community; 4) seasons; 5) flux; and 6) synchronicity. Implications for future research, career theory development, and counselling practice are discussed.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".