Making sense of high potential, talent, and leadership in organizations: a discursive and psychological approach
Bibliographic record
Abstract
Despite the increased attention directed toward high potential and talent in the world of work, conceptual and empirical research is lagging and is needed to better understand what these concepts represent and how they can be predicted (Dries, 2013; Silzer & Church, 2009). The present dissertation sought to address these gaps using discursive and psychological approaches. In Study 1, semi-structured interviews were conducted with executive and senior leaders from a Canadian post-secondary institution to understand how they made sense of and gave sense to high potential and talent. I analyzed transcripts from 20 participants using discourse analysis. The analysis revealed that ‘high potential’ was in the initial stages of entering the focal institution’s discourse and tied to the concept of ‘leadership.’ Talent was used in a general sense to depict successful, skilled, or accomplished individuals. Leadership books and their corresponding ideas served as discursive resources that were used by participants to reshape, legitimate, and contest the shifting meaning of leadership that was occurring in the focal institution and to define the meaning of ‘high potential leadership.’ Moreover, the leadership books (and the associated ideas) were embedded within leadership development programming and other HR practices in the institution. In Study 2, associations between distinct dimensions of cognitive complexity (i.e., differentiation and integration) with leadership level and high potential recommendations were examined in a sample of mid- and senior-level leaders from the aforementioned post-secondary institution. Using two novel computer-assisted software programs (i.e., Profiler Plus & Automated Integrative Complexity), participants’ responses to six questions on the topic of leadership were content analyzed to assess the extent to which their cognitive representations were differentiated and integrated. As expected, participants holding senior leadership positions possessed lower differentiation and higher integration than mid-level leaders. Furthermore, mid-level leaders possessing higher differentiation and lower integration were provided with more high potential recommendations from senior leaders. I discuss the findings of this work within the context of how cognitive complexity may be a valid predictor of high potential leadership across its shifting conceptions.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.013 | 0.098 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".