Methodological reflections on tracing networked images
Bibliographic record
Abstract
Purpose Many scholars highlight a need for reflexive methodological accounts to support visual research. Therefore, this paper offers detailed reflection on the methods involved in tracing and analysing 248 commercial images of entrepreneurship. This account supports our published work examining entrepreneurial masculinities and femininities, which conceptualised the gendering of entrepreneurial aesthetics, and proposed the significance of image networks in the reproduction of neoliberal ideals. Design/methodology/approach Now based on further methodological reflexivity, we offer insights on both the possibilities and challenges of tracing networked images by reviewing four methodological complexities: reflexive engagement with online images; working with and across platforms; tracing as a potentially never-ending process and montage approaches to analysis. Findings Our account focuses on a specific form of imagery – commercial images – on a certain representation – the gendered entrepreneur – and on a particular complex site of encounter – online. This work mapped a visual repertoire of gendered entrepreneurship online by tracing visual constructions of entrepreneurial masculinity and femininity. In this paper, we open the methodological “black box” of our study and explain our belief that methodological advances can only be built through exposing our working practice. Originality/value Through our detailed reflective account, we aim to open discussions to aid development and use of complex visual methods online.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.103 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.064 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".