Bibliographic record
Abstract
As an urban-regional geographer, Dr John Harrison has been actively researching England’s urban and regional policies for the past decade, publishing extensively and delivering presentations (keynote or otherwise) to political leaders and policymakers, most recently in Canada, Germany, and UAE1. In early 2011 he received funding from his institution to conduct an independent study into Local Enterprise Partnerships – joint local authority-business bodies brought forward by groups of local authorities to support local economic development across ‘functional economies’. Extending previous research on the evolution of city-regionalism in England, this research project was uniquely positioned to offer an ‘in retrospect’, ‘in snapshot’ and ‘in prospect’ take on the establishment of LEPs as the Conservative-Liberal Democrat Coalition Government’s chosen model for subnational governance. The research was conducted at a time of transition: Regional Assemblies had been abolished; Government Offices for the Regions and Regional Development Agencies were being wound down; various rounds of LEP announcements had seen 35 LEPs approved/established; first round decisions for the Regional Growth Fund (RGF) had just been announced; the first round of Enterprise Zones (EZ) had been announced. Furthermore, most LEPs were in the process of either forming their Board or holding their first/second Board meetings.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.122 | 0.027 |
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".