Bibliographic record
Abstract
Brussels, unlike Trieste, did experience a reversal of its linguistic balance during the nineteenth and twentieth centuries. This did not happen suddenly or as a result of violence, as it did in Gdańsk. But it was a change which ran counter to the prevailing trend in the city’s hinterland — unlike the case of Montreal — and, for that reason, it remains contested. Belgium is unique in Europe as a state where the everyday language of most of the population of the capital city differs from that of the majority in the country as a whole. Throughout much of its long history Brussels was a city where the Flemish majority spoke the local Brabant dialect of Dutch. French was also long established in the city, having been the language of the court and government since the fifteenth century. But as late as 1788 a Dutch dialect was still the main language of at least 85 per cent of the population. By 1846 that proportion had had fallen 67 per cent and, a century later to 24 per cent (Table 5.1). Since 1947 the Flemish voice in Belgian politics has ruled out any further official language census, but estimates based on surveys and on voting behaviour suggest that the proportion of the Brussels population which speaks mainly Dutch may now be as low as 15 per cent. 33 Articulate elements of the Flemish population, with considerable support in Flanders, would like to reverse this trend, while the contemporary situation is complicated by the large and growing number of non-Belgian residents, which includes both elite Europeans employed by the European Union and immigrant workers from north African countries and elsewhere. These 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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.004 |
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".