Identifying Base Erosion Through the Expenses Localness Indicators Model: A Methodology for Supporting Social Investment
Bibliographic record
Abstract
A company’s base, or physical location, is often a criterion or condition for inclusion in regional development programmes that offer investment incentives such as reduced taxes, subsidised loan rates, or funding for research and development projects. However, these programmes, aimed at strengthening communities lagging behind in economic development, are often the target of malicious exploitation by companies that have a virtual headquarters in the region without actually contributing to local economies. This study proposes the use of the Expenses Localness Indicators (ELI) model as a reliable indicator of a company’s real contribution to a local economy. The ELI model can measure and highlight attempts to erode a company’s headquarters, and also assess a company’s integration into the local economy. By simulating a virtual economic environment and generating synthetic transaction data, the effectiveness of the ELI model in detecting false location claims and quantifying regional participation is evaluated. The results show that companies that prioritise local partnerships maintain higher locality scores, while those that partner with low locality entities weaken their local economic footprint, regardless of physical location. The ELI model provides a transparent and reliable tool that can be used both to grant regional incentives and to monitor their performance. Its integration into policy design can support more equitable, evidence-based approaches to regional economic development and social investment.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".