"What my Guidance Councillor Should Have Told Me": The Importance of Universal Access and Exposure to Executive-Level Advice.
Bibliographic record
Abstract
Often, knowledge and quality education is reserved for the elite, where there are systemic obstacles to gaining access to today’s leaders. Gender and racial inequities in executive-level positions across North America have been a longstanding debate amongst scholars and policy makers. Research has consistently documented that women are disproportionately represented in upper management and in positions of power and still continue to dominate traditionally “female” occupations, such as administrative support and service workers. Though gender inequalities are evidently present, there is also a clear under-representation of visible minorities holding executive-level positions as well. In order to reverse these trends, governments across North-America have enforced employment equity legislation and many organizations have voluntarily committed to similar initiatives. Perceived educational and career-related barriers to opportunity, choice, and information within these segregated groups are shaped early on. For this reason, many researchers champion early interventional programs in order to prevent such perceived barriers from developing. In this paper, there will be a discussion of social networks and how certain groups are denied access to sources of social capital, thus hindering their ability to seek out prospective jobs or entering certain career streams. In this study, Women in the Lead, a database published in 2009, is a national directory of women whose professional expertise and experience recommend them as candidates for positions of senior level responsibility and as members on corporate boards. The Women in the Lead database was comprised entirely of professional women who had voluntarily subscribed as members. Of the 630 women asked to participate, 210 responded to the survey. The 210 women who responded were from 14 different industries in Canada and the United States. The next generation was described as soon to be graduates of high school. A summary of this advice is reported in this paper, with the objective of providing guidance to the next generation looking to enter the workforce, regardless of their gender, location, and race. We also explore the potential of the internet in levelling these barriers and opening up new possibilities for e-mentoring youth and building social capital.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".