In the Midst of Hiring: Pathways of Anticipated and Accidental Job Evolution During Hiring
Bibliographic record
Abstract
In this paper, we examine the evolution of jobs in the midst of the hiring process: how jobs change between the decision to bring in someone to do a body of work and hiring someone. We analyze data from interviews, observations, and documents about start-up hiring and find that, during hiring, tasks are added and removed from jobs; jobs are abandoned, replaced, and moved; and hiring processes are relaunched. We describe two pathways that this evolution takes: the pathway of anticipated evolution, shaped by the unknown nature of the jobs being filled, and the pathway of accidental evolution, shaped by unanticipated factors surrounding jobs. Although the pathways lead to many of the same immediate consequences, there are differences in the longer-term consequences. Across the pathways, many jobs continue to evolve. On the pathway of anticipated evolution, many job incumbents leave within a year and are not replaced. On the pathway of accidental evolution, the longer-term consequences for job incumbents, structures, and organizations range from stability in structures and incumbents to ongoing conflict and incumbent departure. Not surprisingly, most evolving jobs are new to their organizations, but contrary to common conceptions, job evolution is not the product of managers who lack experience or use lax hiring practices. Our observations provide evidence of the emergent nature of jobs, hiring, and organizations.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".