Reflections on a (Failed) Launch: Revisiting and Reflecting on “Territoriality in Organizations”
Bibliographic record
Abstract
Despite being a novel and useful construct, perhaps prophetically, territoriality has defended itself from adoption by the management research community. However, the topic itself is not to blame and looking back 20 years after publishing the first management article on territoriality, I realize that certain choices I made influenced the direction and impact of my initial publication. In this article, I reflect on my experience trying to launch a new topic (territoriality) into the management field. From this experience, I offer readers ideas that may help successfully launch new constructs. Failure to join other conversations, develop a measure that could be easily adopted by others, and key early tradeoffs I made played a key role in my own failed (slow) launch.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.058 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.076 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.014 | 0.041 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".