Bibliographic record
Abstract
Mandatory offsets are policy instruments to leverage defence procurement projects to conduct industrial policy. The prime contractor commits to generate new business in mutually acceptable sectors equivalent to a large percentage of the project value. Offset multipliers “relax” this constraint by discounting the prime contractor’s offset obligations if investments flow to sectors prioritized by the purchasing country’s industrial policy objectives. This endogenizes the relationship between the original project and the offset contracts. This paper provides a new theoretical analysis of the following three questions that have gone unaddressed in the literature. First, the efficiency of such policies depends on the absorption capacity of a targeted industry. If this capacity is low, import substitution is expensive and the prime contractor may rather choose to invest elsewhere in the economy to satisfy the overall mandatory offset constraint thereby thwarting the original objective. Second, whereas a uniform relaxation of offsets through multipliers can reduce distortions introduced by mandated offsets, multipliers may enhance distortions as an unintended consequence. Third, the prime contractor’s response to offset credit incentives may be weak due to transaction costs arising from having to find new domestic partners to satisfy the offset requirements and manage the contracts.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".