Maura Sellars: Educating students with refugee and asylum seekers experiences. A commitment to humanity, Opladen – Berlin – Toronto 2020, Verlag Barbara Budrich, pp. 170 ISBN: 9783847422891
Bibliographic record
Abstract
The 2019 edition of the joint OECD, ILO, IOM & UNHCR International Migration and Displacement Trends and Policies Report shows that by mid-2018, despite decreasing numbers of refugees entering the European Union and Turkey, the global refugee population had reached 25.7 million (EASO Annual Report..., 2019).We are not able to hold people back from escaping a bombed country with a totalitarian regime where their health and/or life is in danger.The process of becoming/being a refugee is complicated and multi-staged in its nature (pre-emigration phase; escape; reaching the country of first asylum; settling in a new country; post-migration/repatriation phase (return to the home country) (Grzymała-Moszczyńska 2000).A change of place of residence is connected with the constant accumulation of new, sometimes very difficult, experiences at various levels of human functioning.John Berry's theory of acculturation shows that the process of entering the circle of a different socio-cultural reality is long and multi-faceted (Barry 2003).Every change of residence place, in a more or less violent way 'forces' the need to 'move' within a different culture and social reality and to interact with people with various 'software in the minds' -using Geert Hofstede's language (Hofstede G., Hofstede G.J., Minkov M., 2010).Students with asylum seekers and refugee backgrounds often leave schools early, which is caused by many difficulties in their path (poor linguistic competences in the field of new language, cultural differences, including conflict of values, school backlogs, unstable life situation, insufficient support system at school, etc.Because of that, some of them become part of the so-called excluded generation (Telles, Ortiz 2008).Education is an important factor of the wellbeing and integration of students with refugee and asylum seeker experiences.Therefore, it is more and more important to conduct empirical research and theoretical analyses in this field.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".