Houkutteleva Helsinki – Sosiaaliset tekijät, jotka vaikuttavat kansainvälisten tulokkaiden tuntemuksiin uudesta kotipaikastaan
Bibliographic record
Abstract
Finland requires international talent to immigrate to the country due to its low birth rate and consequentially weakening economy. Although the Finnish government and employers recognize the issue, the actions proposed to alleviate the situation do not sufficiently consider the successful retention and attraction of international newcomers. This leads to newcoming immigrants finding it hard to settle and find local contacts and friends in Finland. To enhance the attraction and retention of immigrants and thus grow the number of international talent moving and living in the capital region, the City of Helsinki plans to release a social befriending program to enhance its newcoming immigrants’ feeling of belonging. To assist the development of the abovementioned program, this thesis pursues to identify social factors present in the capital region-residing newcomers’ lives that hinder them from successfully settling into their new environment and building fulfilling social lives. It does this by examining related research and similar services, as well as conducting a qualitative study on the potential users of the befriending program. The thesis also argues for the relevance various design mindsets and practices hold for the development of such a public and societally significant service. The study began with a presurvey targeted at capital region-residing immigrants. Based on the survey answers, a group of 20 participants was selected to join either a series of interviews or workshops mapping out the participants’ experiences settling in Finland. After treating and anonymizing, the study data were analyzed using the affinity diagramming method. The results list a total of seven social factors affecting newcoming immigrants’ lives in Finland and the capital region. The factors highlight especially the newcomers’ need for a vivid and active community, accurate information, and peer support related to life in Finland. In addition, the study finds that newcomers should be provided an unintimidating yet realistic image via marketing and media channels of the befriending program. This is due to many newcomers struggling with the closed Finnish culture and society. The final study findings are presented as a list of suggestions that the City of Helsinki should consider when establishing the befriending program in the near future to increase the retention and attraction of international newcomers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".