Rethinking professional identity formation amidst protests and social upheaval: a journey in Africa
Bibliographic record
Abstract
The under-representation of minoritized or previously oppressed groups in research challenges the current universal understanding of professional identity formation (PIF). To date, there has been no recognition of an African influence on PIF, which is crucial for understanding this phenomenon in places like South Africa, a society in which the inequity of the apartheid era still prevails. In addition, there is little data examining how social upheaval could impact PIF. This study uses interviews with medical students to explore PIF within the context of social upheaval during the 2015-2016 protests that rocked South Africa when students challenged asymmetries of power and privilege that persisted long after the country's democratic transition. The combination of the primary author's autoethnographic story, weaved into the South African sociohistorical context and ubuntu philosophy, contributes to this study of PIF in the South African context. The use of an African metaphor allowed the reorientation of PIF to reflect the influence of an ubuntu-based value system. Using the calabash as a metaphor, participants' experiences were framed and organized in two ways: a calabash worldview and the campus calabash. The calabash worldview is a multidimensional mixture of values that include ubuntu, reflections of traditional childhoods, and the image of women as igneous rocks, which recognizes the power and influence on PIF of the women who raised the participants. Introducing an African ubuntu-based perspective into the PIF discourse may redirect the acknowledgement of context and local reality in developing professional identity.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.036 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".