Illustrations of ethical dilemmas during ethnographic fieldwork: when social justice meets neoliberalism in adult education
Bibliographic record
Abstract
This article explores the ethical dilemmas encountered by two ethnographers in adult education research in a context where neoliberalism impacts on education settings, policies, and social justice. Everyday ethical dilemmas arise in thorny situations in which the general ethical principles ethnographers are regulated by cannot help them react or respond in the heat of the moment. The three technological processes through which neoliberalism in education is operationalised (the market, management, and performance) are used to analyse two ethnographic research contexts in adult education settings in Québec, Canada. The empirical data generated from these two separate investigations are used to construct six vignettes illustrating how neoliberal technologies influence social justice in those settings. Neoliberalism, precarity and social justice are closely related both theoretically and in our results. The data show the sensitivity required by the ethnographer to navigate the precarious situations that individuals and organisations face vis-a-vis neoliberalism in adult education.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".