Zebras showing their stripes: a critical sense-making study of women CSR leaders
Bibliographic record
Abstract
Purpose The purpose of this research is to reveal the gendered nature of social arrangements in order to bring to the surface the hidden discourses that mediate the opportunities of women leaders in the field of corporate social responsibility (CSR) and sustainability. Design/methodology/approach The author uses critical sense-making (CSM) to analyze interviews with CSR leaders toward understanding the interconnected layers of influences they draw from as they make sense of their experiences. Findings Despite the positioning of women as being untapped resources within CSR, the reality within CSR leadership indicates that resilient, stereotypical social constructions of gender are being (re)created. However, cues can disrupt the ongoing process of sense-making and create shocks that represent opportunities for resistance as discriminatory practices are revealed. Research limitations/implications Applying CSM as a methodology and to the field of CSR adds a component to CSR and gender scholarship that is currently missing. CSM as a methodology bridges broader sociocultural discourses and the local site of sense-making, making visible the structures and processes that enable some narratives to become legitimized by the formative context and protect the status quo. Social implications If these leaders are able to use their discursive power to establish an alternate, dominant narrative throughout their organizations – a culture of emotional empathy within CSR – alternate meanings about the nature and purpose of CSR may emerge while highlighting the need for change. Originality/value Applying CSM as a methodology and to the field of CSR adds a component to CSR and gender scholarship that is currently missing. CSM as a methodology bridges broader sociocultural discourses and the local site of sense-making, making visible the structures and processes that enable some narratives to become legitimized by the formative context and protect the status quo.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".