Intersectional Influences of Socioeconomic Status, Gender, and Location on Education Consultants’ Opportunities and Experiences
Bibliographic record
Abstract
Abstract This chapter presents the findings from an exploratory mixed-methods study that examined the significance of social location(s) and intersectionality in shaping the opportunities and experiences of an international sample of individuals with experience working as contract-based education consultants. Education consulting is a growing field, attracting entrepreneurial professionals from practitioner and academic communities around the world; however, very little research exists on this diverse and diffused group of workers. The research sought to answer two questions: (a) What is the influence of social identity and social position(s) on education consulting opportunities and experiences? (b) What benefits and challenges do educational development consultants experience in their work? Insights from feminist intersectionality theory guide the analysis and discussion. The central argument made, based on the findings from the online survey and interviews with consultants, is that social location – particularly as related to socioeconomic status, gender, and geographic location – and while temporally and spatially contingent are perceived to be the most significant and intersecting factors shaping who secures contracts as well as the nature and value of such experiences for individuals’ personal and professional development and professional impact overall.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".