in exile/in flight: Two Poems From a Poetic Autoethnography of Academic Banishment
Bibliographic record
Abstract
From 2008 to 2010, I was assistant professor in the Division of Creative Arts in Learning at Lesley University in Cambridge, Massachusetts. For various reasons—including my older son’s need to complete his final 2 years of high school in Canada—I commuted to and from my family home in Victoria, British Columbia to Boston. The position itself, teaching within a graduate program in arts integration, also called for substantial travel as the program is carried out in over 20 states. During this challenging time, I met many friends and colleagues who told me of their own periods of academic exile. This seems to be a common occurrence, as scholars are most often not able to choose where they work. Although we are highly privileged members of the social elite, we share this experience with migrant workers around the world who are forced, due to economic duress, to spend long periods away from loved ones.
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How this classification was reachedexpand
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".