Bibliographic record
Abstract
Conventional non-parametric efficiency measurement relies on superlative index number based measures of total factor productivity. In many instances, this measure is decomposed into various components such as scale effects and technological change. The decomposition usually requires estimation of statistical cost or production function. This approach has a variety of drawbacks. Firstly, it is based on a “non-frontier ” notion of efficiency. Secondly, it usually requires behavioral assumptions such as profit maximization or cost minimization, and lastly, it requires data on both prices and quantities of outputs and inputs. In recent years, attention has shifted towards alternatives such as Data Envelopment Analysis (DEA). DEA does not require assumptions such as profit maximization or data on prices and has been used to measure performance of non-profit organizations. DEA based studies typically use cross-sectional data and therefore, unlike conventional index number based studies, are unable to analyze changes in productive efficiency over time. This paper uses panel data on 22 U.S. airports over the five-year period 1989-93 to construct a Malmquist index of productivity change and decomposes it into scale effects, efficiency effects and technical change. The paper also explores the nested relationship between airside efficiency and terminal efficiency and the tradeoffs between lower costs and higher revenues.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".