Navigating the Academy: Using Memory-Work to Chart a Way Forward
Bibliographic record
Abstract
This article explores our use of memory work, as five experienced self-study researchers. Utilizing the power of memory-work and in the spirit of self-study collegiality, we aimed to understand how our collective experiences might influence current and future possibilities for ourselves, and importantly, for others. Our recursive process involved writing, in the third person, detailed memories evoked by six prompts which included reflections on being mentored and navigating the volatile, uncertain, complex, and ambiguous (VUCA) environment in tertiary institutions. We shared our writing via Zoom, and engaged in collective dialogic analysis. Our findings revealed significant discrepancies between institutional values and our personal values. An examination of our memories underscored the importance of teaching and self-study research to us, though not necessarily to our institutions. We felt that self-study research was not considered of equal importance by our institutions or colleagues. However, we maintain that measures of academic success, as defined by institutions, should not dictate how we measure our own success as academics or as humans. It is important to remain lifelong learners, staying curious, and being true to our own values. Ultimately, the importance of fostering supportive, reciprocal relationships and practicing kindness, to ourselves and others, was central to our reflection. We hope that our findings resonate with other academics.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.019 | 0.050 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 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".