Bibliographic record
Abstract
We begin our discussion of the empirical results with a closer look at the four work logics. Figure 7.1 shows the distribution of individuals across the horizontal division lines to which we give heavy emphasis in the construction of our class schema. Although the four countries in our sample do not present fundamentally diverse employment structures, substantial differences emerge. The markedly industrial bias of the German economy is reflected in a predominant share of individuals evolving in a technical work logic: more than a third of Germany's workforce are technical experts, technicians, crafts workers and operatives. This contrasts with data for Britain where only a quarter of the labour force is employed in these classes. Britain's employment, however, clusters more heavily in the organizational work logic than the three other countries: 20 per cent of the British labour force work in managerial or associate managerial occupations and 15 per cent in clerical office jobs. In the case of Sweden, Figure 7.1 clearly reflects the importance of the country's welfare state. A third of Sweden's employment is set in the interpersonal service logic. The large proportion of individuals in social services is compensated by Sweden's low share within the organizational work logic. The finding that Sweden is comparatively 'undermanaged' is not new and has, among others, been accounted for by the country's large public sector (Ahrne and Wright, 1983: 223). Distribution of total employment across the four work logics (in %) KeywordsClass StructureEmployment ShareWork LogicSkilled CraftCraft WorkerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".