Cultural participation during the spring lockdown of 2020 in France
Bibliographic record
Abstract
The lockdown due to the out-break of Covid-19 has changed the everyday life, modifying timetables, working patterns and schooling at the same time. For a while people have been deprived of ‘in-real-life’ cultural experiences: it was indeed impossible to visit a museum, or to go to a cinema as many premisses dedicated to culture, as well as many shops selling cultural goods, were temporarily closed. Spending more time at home meant one could allocate more time using digital resources on the other hand.The current study shows what cultural participation looked like during these weeks of lockdown in spring 2020 in France. It was carried out thanks to a special wave of a recurrent survey led by Credoc that coincided precisely with this moment in time in 2020 (the French institution Credoc – Centre de recherche pour l’etude et l’observation des conditions de vie – collects data on living conditions every quarter of the year from a sample of circa 3000 individuals aged 15 and above). The data from the 2018 survey on cultural participation have also been included in the analysis in order to put the 2020 lockdown situation in perspective with a recent and more usual situation.It appears that people seized the opportunity to stay at home to develop their own creativity and self expression playing music, drawing, painting, writing a diary or a novel for instance and those who did were younger and from less privileged social backgrounds than usual. On average, cultural participation kept steady and even became more prevalent in social groups which were previoulsy less accustomed to cultural experiences. This latter statement is particularly noticeable for playing videogames and watching online videos, but not for reading books nor for reading the newspapers. Social networks became more popular, especially among older people (people aged 60 and above). The environment in which people were living at that time also had an influence on their cultural behaviour.All in all, it seems that in spite of a tough economic situation which led to widening the gap between social groups in terms of social inequalities, the outcome is slightly different when it comes to cultural participation. The time spent home enable to somehow bridge the gap in terms of cultural participation and cultural habits between the young and the elderly and between social classes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".