An ethical framework for coaching research among vulnerable groups: paving the way to a more inclusive approach
Bibliographic record
Abstract
Scholarship on vulnerable groups in coaching is growing.Yet, there are no specific ethical frameworks focused on guiding researchers and practitioners in researching coaching among vulnerable groups.This theoretical article addresses this gap by developing a framework to depict the multiple dimensions of ethical decision-making when working with vulnerable groups in coaching research.It contends that focus on the tensions emerging through the dual role of practitioner and researcher in coaching research is key to navigating ethical decision-making.The article also identifies 'vulnerable groups' as worthy of study within coaching research as a means to bolster the evidence base for coaching and to address scholarship's biases towards more privileged groups.
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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 | 0.262 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.030 | 0.179 |
| Scholarly communication | 0.032 | 0.030 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.023 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 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".