Academic public intellectuals’ lives : negotiating the borderlines
Bibliographic record
Abstract
This study presents the life stories of four selected Ethiopian public intellectuals among the diaspora. The overall study is presented in a form of fictionalized narrative based on the entire life experiences of the intellectuals. It is framed using the political economy of Ethiopian higher education as its context. Using fictionalized narratives in educational research is a relatively recent phenomenon. In the larger debate of the fact-fiction distinction (Brockmeier, 2013), the notion of panfictionality suggests that it is hardly possible to draw a hard and fast dividing line between representations that are labeled as fact and fiction (Brockmeier, 2013). This allows for the possibility of presenting real-life stories in a form of fiction. The intellectuals in this study are selected because they had worked in the academy in Ethiopia, are currently employed in a tenure-track position in North America, and are engaged in addressing the public both as an academic as well as public figure. They were asked to participate in the life story interviews which are informed by the notions of public intellectuals and Jakobson’s (2012) symmetric-criticality framework. Interview transcripts were sent back to the participants for accuracy and validity. The author’s subjectivity, positionality, and other ethical issues are addressed to meet institutional requirements. The fictionalized characters narrate stories related to academic freedom, public intellectualism, and speech and silence. They narrate vibrant stories. They tell their stories and speak out about issues that matter to them.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.026 | 0.030 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.006 |
| 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".