Public Libraries in Norway Help Non-Western Immigrant Women to Integrate into Society
Bibliographic record
Abstract
Abstract
 
 Objectives – To discover the ways in which the public library was used by immigrant women, with a particular focus on the library as a meeting place. 
 
 Design – Semi-structured qualitative interviews conducted in the participants’ native languages. 
 
 Setting – Public libraries in Norway. Participants lived in one of two cities both with a population of approximately 40,000 and a somewhat lower number of immigrants than the national average.
 
 Subjects – Nine non-western women who had immigrated to Norway between 8 months and 17 years prior to the study. Three women were from Iran, Kurdistan and Afghanistan respectively. All identified themselves as public library users. 
 
 Methods – Participants were interviewed in their native languages and the qualitative results were analyzed in accordance with the theoretical framework set out by the authors. The main areas of focus were the role of the library in the generation of social capital, and the library as a high intensive versus low intensive meeting place.
 
 Main Results – Participants used public libraries in various ways. In the initial stages of life in a new country they were used to observe and learn about the majority culture and language. They were also used as a safe place to openly grieve and provide comfort among close friends without fear of being seen by other fellow countrymen. Over time, participants came to use the library space in more traditional ways such as for information, social, and professional needs. The study also revealed that using public libraries built trust in the institution of libraries and librarians as employees. 
 
 Conclusions – The public library plays a key role in the generation of social capital, both in terms of integrating into the majority culture through observation and spontaneous interactions (bridging social capital) and connecting with others from participants’ home cultures (bonding social capital) for example through the provision of social space and other links to home such as native language materials. The public library was used initially by participants as a high intensive meeting place, for meeting with friends and consoling one another. In time, however, the public library became a medium- to low-intensive meeting place, with participants engaging in library programmes or in spontaneous interactions with other library customers. Moreover, the study refutes the Swedish Library Association’s conclusion that library is used more often by immigrants that have lived in the country for long periods of time.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.558 |
| 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; both teacher heads agree on what is shown here.
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".