Falling Back into Teaching: A Triptych of Teachers' Motiviations, Decision and Consequences
Bibliographic record
Abstract
You can always fall back into teaching. This thesis explores the meaning of this statement as it pertains to teachers’ motivations related to their careers, the decisions they make in both their daily work and their professional goals, and the consequences of those decisions. I investigate why teachers choose to teach. What are the reasons that lead teachers to ‘fall back’ into teaching? Upon beginning their career, what do these teachers experience during their daily work in the classroom? How do they negotiate how they feel with what they do? Falling back into teaching is an arts-informed thesis. I am an artist and a researcher who communicates in text and images. I combine autobiographical writing and the language of art, the elements of design, to explain my academic and artistic journey. The thesis employs the metaphor of a triptych, a three-paneled painting that has been and continues to be used specifically by visual artists. The left panel encompasses the introduction; a definition of 'fallback', an explanation of arts-informed inquiry as a method for researching fallback, and a first meeting with my parents and me who inform the thesis. The middle panel follows my research process in understanding 'fallback' using the elements of design: line, shape, space, colour, value and texture. The final panel provides a reflection on the process and a response to those who have read and relate to 'fallback'.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".