The South African labour market, 1995-2013
Bibliographic record
Abstract
This paper investigates the changes in the South African labour market in the post-apartheid period in 1995-2013 by updating the work by Oosthuizen (2006) and Yu (2008). The three main data sources used are the October Household Survey of 1995, the Labour Force Survey of September 2004 and the Quarterly Labour Force Survey of 2013 Quarter 4. It was found that while unemployment has risen over the period, employment has also increased. Nonetheless, the extent of employment increase was not rapid enough to absorb all net entrants in to the labour force, resulting in increasing unemployment, or an employment absorption rate of below 100 per cent. Unemployment continues to be concentrated in specific demographically and geographically defined groups, most notably blacks, the poorly educated and the youngsters residing in Gauteng. Unemployment is a chronic problem for the youth in particular, as nearly three quarters of them never worked before. Finally, the employment absorption rate was the highest in some less developed provinces like Northern Cape, Mpumalanga and Limpopo, thereby suggesting the possible success of the government’s efforts to promote the development in the poorer provinces.
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 | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".