Rich Pictures in Qualitative Research in Higher Education: The Student as Consumer and Producer in Personal Branding.
Bibliographic record
Abstract
Marketing principles and consumerism are evident in higher education with universities central to the development of fit for purpose graduates. Students are increasingly viewed as consumers of university products and expected to manage self-hood and to promote themselves to the marketplace. This article is drawn from research in an ongoing larger scale project exploring the ownership of students in shaping their 'career capital' and in building 'brand-me' from a student perspective when seeking industrial placement and graduate career progression. It appraises the use of a 'soft systems' methodology using rich pictures (RP) to support qualitative one-to-one interviews with students in higher education. The findings showed that the combination of in-depth interviews with the rich pictures creative qualitative approach provided a much closer generation of insights to inform staff in the support of students pursuing of industrial placement and career progression, and for the students it offered an opportunity for self-reflection and consideration of 'brand-me'.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".