Resource Commensurability and Ideological Elements of the Exchange Relationship
Bibliographic record
Abstract
Our understanding of the employee–organizational relationship (EOR) (Coyle-Shapiro, Shore, Taylor, & Tetrick, 2004), as well as other behavioral theories or frameworks related to the employment relationship, is heavily based on the implicit notion of a “standard” employment arrangement (e.g., Ashford, George, & Blatt, 2007; Gallagher & Sverke, 2005; George & Chattopadhyay, 2005; Pfeffer & Baron, 1988; Rousseau, 1997). The EOR draws upon social exchange theory (Blau, 1964) and the inducements– contributions model (March & Simon, 1958) to explain why workers respond to their employers’ actions by engaging in reciprocal behaviors. As such, the EOR forms the foundations of many fundamental theories underlying worker behaviors (e.g., psychological contract, perceived organizational support, leader–member exchange, organizational commitment). However, this begs a theoretical and practical question: What if the worker is not an “employee” per se, and what if the employer is potentially a client or even a series of clients, sometimes found through an intermediary?
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".