Making the Next Move: How Experiential and Vicarious Learning Shape the Locations of Chains' Acquisitions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examine acquisitions by multiunit chain organizations to determine why they acquire a particular target rather than others that are available to them and thus better understand chain growth. We advance experiential and vicarious learning processes as an explanation for chains' next spatial move. Our analysis of Ontario nursing home chains' acquisition location choices from 1971 to 1996 provides broad support for a learning perspective, demonstrating how experiential and vicarious processes shape and constrain the locations of chains' acquisitions. Experiential processes lead chains to replicate themselves by acquiring components geographically and organizationally similar to their own most recent and most similar prior acquisitions and their own current components. Vicarious processes lead chains to imitate location choices of other visible and comparable chains' most recent acquisitions, prior acquisitions nearest to potential targets, and their current components. Our study thus establishes organizational learning as a conceptual foundation for predicting the location of a chain's next acquisition and, more generally, the spatial expansion of chains over time.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it