An Intra-organizational Ecology of Individual Attainment
Bibliographic record
Abstract
Drawing parallels to niche theory in ecological perspectives, this article develops an intra-organizational conceptualization of the niche that is grounded in the activities of the organization. Niches are constructed from mapping individuals to formal and informal activities. Because the many activities within organizations are difficult to observe, we propose a novel empirical strategy to characterize niches: we exploit the complete roster of memberships in electronic mailing lists. We characterize niches along four dimensions: competitive crowding, status, diversity, and typicality, and we develop theoretical propositions about the resources that accrue to occupants of niches that vary on these dimensions. Propositions are tested in two, disparate empirical settings: the R&D laboratory of a biopharmaceutical company and an information services firm. Results indicate that, across both settings, people in competitively crowded niches have less influence in the communication network and achieve lower levels of attainment, whereas those in high status and diverse niches have more influence in the communication network and achieve higher levels of attainment. We find suggestive evidence that employees who deviate from the identity blueprint for their job role also garner fewer rewards.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".