Intersectionality in Talent Management: Broadening our Sight for More Inclusive Theorizing
Bibliographic record
Abstract
The extant work on talent management has largely promoted neoliberal agendas concerned with ranking, rating, and recording employees’ talent, or indeed lack of talent. While there are emerging insights that unravel the gendered, racialized, and classed logics underpinning dominant TM writings, there is largely an acceptance of TM as a philosophy, and there has been limited work that challenges the epistemological foundations of TM. We argue that talent management philosophies that have strengthened instrumentalism are conceived as a managerial tool that showed a commitment to capitalistic frameworks, and ignored critical management studies, which stress the importance of resistance, and the power relations that shape, constrain, and may hinder opportunities for all employees. In line with this year’s AOM theme “Broadening our Sight”, this presenter symposium aims to broaden our sight for more inclusive TM theorizing by including five papers that bring intersectionality to the forefront. The collection of papers documents the voices of the silenced talent from the Netherlands, Germany, Switzerland, Austria, Ireland, New Zealand, Finland, and Ghana, and draws on feminist, critical, transnational and postcolonial epistemologies to challenge the dominance of masculinist and neo-liberal logics in TM theorizing and open up opportunities to review TM systems that stress inclusion and equity. This is a timely endeavor to draw out, extend, give emphasis and voice to what and who is silent or marginally present or ideologically represented in much of the current TM literature with the aim of broadening our sight in TM theorizing. Talent Management: Re-Imagining Transnational, Intersectional, and Post-Colonial Agendas Presenter: Beverly Dawn Metcalfe; American U. of Beirut Presenter: Yasmeen Makarem; American U. of Beirut Presenter: Fida Afiouni; American U. of Beirut For Whom Does Talent Management Make Sense? Presenter: Sanne Nijs; Human Resource Studies, Tilburg U. Presenter: Edina Dóci; Vrije U. Amsterdam, School of Business and Economics Presenter: Joost Luyckx; KU Leuven Tapping into Marginalized Talent: Examining the Work and Career Experiences of LGBTQ Employees Presenter: Caroline Straub; Bern U. of Applied Sciences Presenter: Pamela Lirio; U. of Montreal Presenter: Barbara Beham; Berlin School of Economics and Law Talent Management Theory & Practice in Public Organizations Presenter: Alma M. McCarthy; National U. of Ireland - Galway Presenter: Katerina Bohle Carbonell; National U. of Ireland Presenter: Turo Virtanen; U. of Helsinki Presenter: Paula Marie O'Kane; U. of Otago Presenter: Denise Holland; National U. of Ireland Presenter: Monty Van Wart; California State U. San Bernardino Presenter: John Burns; U. of hong kong Unfolding the Existence of the Colonial System in TM Practices – A Theoretical Perspective Presenter: Zinabu Shaibu; zinabu Presenter: Mustafa B Ozturk; Queen Mary U. of London Presenter: Ahu Tatli; U. of London
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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 | 0.035 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.012 | 0.088 |
| Scholarly communication | 0.045 | 0.067 |
| Open science | 0.005 | 0.040 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".