Exploring the authenticity, or lack thereof, of the discourse of talent management
Bibliographic record
Abstract
Purpose This paper aims to examine the ways in which discourses of talent management (TM) reinforce and perpetuate structural barriers of exclusion and discrimination. The argument is made that dominant TM discourses must be interrogated if authentic talent development (ATD) practices are to succeed. This interrogation will require a shift from an organizational emphasis on talent identification towards ATD’s focus on talent cultivation. Design/methodology/approach A conceptual approach is used to critically analyse TM discourses to assess the degree to which they are inclusive. Building upon the work of Debebe (2017), an alternative ATD approach is suggested that, together with the novel concept of authentic otherness, may enable scholars and practitioners to reflect upon current organizational practices and devise new approaches that encourage talent cultivation in diverse employees. This, in turn, may foster a greater sense of organizational belonging. Findings Findings identify a number of ways in which organizational norms and structures are maintained and perpetuated through dominant, mainstream TM practices. This hinders ATD for many due to social ascription processes. By exploring the concept of “authentic otherness” (Gardiner, 2017), alongside Debebe’s (2017) approach to ATD, the argument is made that systemic inequities in the workplace may be addressed when we create conditions to support the cultivation of talent for all employees. Originality/value This paper builds on recent arguments in the critical TM literature concerning the exclusionary nature of mainstream TM practices in organizations. The concept of authentic otherness is clarified and defined with a view to using this new term as a heuristic device to encourage a reflective understanding of how ATD practices can be developed.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".