Bibliographic record
Abstract
We analyze a general market for an industry of competing service facilities. Firms differentiate themselves by their price levels and the waiting time their customers experience, as well as different attributes not determined directly through competition. Our model therefore assumes that the expected demand experienced by a given firm may depend on all of the industry’s price levels as well as a (steady-state) waiting-time standard, which each of the firms announces and commits itself to by proper adjustment of its capacity level. We focus primarily on a separable specification, which in addition is linear in the prices. (Alternative nonseparable or nonlinear specifications are discussed in the concluding section.) We define a firm’s service level as the difference between an upper-bound benchmark for the waiting-time standard (w̅) and the firm’s actual waiting-time standard. Different types of competition and the resulting equilibrium behavior may arise, depending on the industry dynamics through which the firms select their strategic choices. In one case, firms may initially select their waiting-time standards, followed by a selection of their prices in a second stage (service-level first). Alternatively, the sequence of strategic choices may be reversed (price first) or, as a third alternative, the firms may make their choices simultaneously (simultaneous competition). We model each of the service facilities as a single-server M/M/1 queueing facility, which receives a given firm-specific price for each customer served. Each firm incurs a given cost per customer served as well as cost per unit of time proportional to its adopted capacity level.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".