Characterisation of the Disadvantaged: Explaining Differences in Levels of Support for Independence by Income Levels, Economic Activity and Socio-economic Status
Bibliographic record
Abstract
This paper specifically seeks to explain why those who are most disadvantaged in Scottish society are more likely to support independence. This focus on the lowest ranking socio-economic classes is relevant for two main reasons: one is that they are shown as consistently more likely to vote for constitutional change and, two, they are generally the least interested in politics and hence an understanding of their motives is necessary if they are to be better engaged with opinion formers. The analysis demonstrates, by contrasting the upper and lower population quarters on a number of socio-economic indicators (income and employment related groups), the differing attitudes which are displayed. Further characteristics of the lower quarter are highlighted. These include their greater tendency to identify themselves as Scottish rather than British and to expect general economic improvement following a yes vote in September. They are less likely to expect a lessening of economic inequality, however, and their recent support for the SNP is no higher than more affluent groups. They are also less likely to take an active interest in politics. This should help inform debate ahead of the 2014 referendum.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".