Bibliographic record
Abstract
Factoids are statements that are repeated so often that they appear as facts even though they are not. I don't know any field that is as full of factoids as working life. And-sadly-there are working life researchers contributing with factoids to what is called the public debate.When I detect factoids of working life, they have one thing in common: They are portrayed as self-evident truths, but they do not have any data to back them up. For example, it was not long ago when this had become a fact: People in general and youth in particular don't want secure employment any more. Earlier, in the old society, people wanted such security, but now, in the new society, nobody wants it any longer. But the statement was false. It was a factoid. It was spread in mass media, most politicians believed in it, and there were even working life researchers who disseminated it. But it was false. It was a factoid. For those who wanted to see, there were numerous empirical studies of what people really thought about secure jobs. They showed clearly that secure jobs were what people wanted and that there was no difference between age groups. But in the public debate, facts succumbed to the factoid.Another example: Labor law, especially the Law on Security of Employment, is stricter in Sweden than in other countries and it curtails the rate of employment. It is false. To begin with, Sweden belongs to a middle category of strictness, that is, the degree of security of employment, in international comparison. At the top of the table of strictness, we find France, Portugal, Turkey, and Spain. At the bottom are for example Japan, Denmark, Canada, UK, and USA. And among those in between are Sweden, Norway, Finland, Germany, and Poland. Further, if the statement was true, those countries that have liberalized their labor market laws should have reached a higher level of employment than other countries. But that is not the case. There simply is not any correlation between the strictness of labor law and the degree of employment (Furaker, 2009; Furaker et al., 2007). The statement is a factoid.A further example: The demands of qualifications in working life have risen to such an extent that people's level of education has not managed to keep up. There is a widespread under-education on the labor market. It is false. In reality, it is the other way around. It is true that there is a big gap between individuals' level of education and the qualification level of the jobs, but it goes the other way as it were: People's level of education has risen to such an extent that qualifications in working life have not managed to keep up. There is a widespread over-education on the labor market (le Grand et al., 2004; Tahlin, 2007). The statement is a factoid.It's hard to stop, here is another one: Equal pay threatens jobs: the smaller the wage gap, the lower the level of employment. Sometimes it is also expressed as a big wage gap is favorable to the number of jobs. But however it is formulated it is false. Let us have a look at the employment and wage differences in 22 countries during 22 years (Barth & Moene, 2012). If the statement was true, the level of employment should be lower in countries with a high degree of equality of wages. But it isn't like that. On the contrary, there is a stable pattern of the level of employment being higher in those countries-like the Nordic ones-that have the smallest wage differences. Further, if the statement was true, the percentage of the population in the labor force should be lower where wage equality is high. …
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".