An Intraorganizational Ecology of Individual Attainment
Bibliographic record
Abstract
This paper extends niche theory to develop an intraorganizational conceptualization of the niche that is grounded in the activities of organizational members. We construe niches as positions in a mapping of individuals to formal and informal activities within organizations. We posit that positional characteristics in this activity-based system are critical determinants of members’ access to information and relationships—two of the vital resources for advancement in organizations. Because activities are difficult to observe, we propose a novel empirical strategy to depict niches: we exploit a census of memberships in electronic mailing lists. We assess three niche dimensions—competitive crowding, status, and diversity—and show that these attributes affect the allocation of rewards to employees. Propositions are tested in two empirical settings: an information services firm and the R&D division of a biopharmaceutical company. Results indicate that people in competitively crowded niches had lower levels of attainment, whereas those in high status and diverse niches enjoyed higher attainment levels. We conclude with a discussion of email distribution lists as a tool for organizational research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| 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".