The Teacher's Career and Life
Bibliographic record
Abstract
Abstract. Participants in a Wabash Center consultation on vocation discussed the variety of expectations, opportunities, and challenges that create contexts for teaching as they move through careers. These essays emerge from the experiences and reflections of four participants about different stages of careers in diverse contexts. Tom Massaro writes from the perspective of one who recently navigated the challenges leading up to the tenure review in a Jesuit theological school and notes common patterns amidst the diversity of challenges. Phyllis Airhart ponders vocational fidelity in the transitions to new roles and responsibilities at mid‐career in a Canadian university. Barbara Brown Zikmund deals with what she calls the ‘mature years’ and traces a major shift in her career from administration in an American school to teaching in Japan. Raymond Williams reflects on vocation during the process of preparing for retirement from teaching in a liberal arts college, attempting to respond faithfully to the inevitable question, ‘What are you going to do when you retire?’ Vocation is a thread that runs through each essay as reflection on the integrity and continuity of careers. The authors raise issues and make suggestions that may help others reflect on their vocation as teacher.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".