Increasing Inclusion and Reducing the Stigma of Special Needs in Latvia: What Can We Learn from Other Countries?
Bibliographic record
Abstract
Latvia regained its independence in 1991 and has been slowly transforming the education system to meet the standards of the European Union (EU) and the Western world. Since regaining independence Latvia has started to integrate children with special education needs into regular schools and society; yet the process is quite restrained and measured, causing many to suggest that there must be a way and means to accelerate this process. If only Latvians could access and use practices found (Alberta) Canada or another inclusive country (Finland), that has successfully integrated students and adults with disabilities into school and society, to diminish Latvian problems such as life long dependency, poverty and social exclusion that adds to an already existing stigma of intellectual disability according to the European Union Monitoring and Advocacy Program (EUMAP, 2005). Stigma is the one issue that keeps surfacing as the key challenge for people with special needs in Latvia (Fine-Davis & Faas, 2014). Latvian society at present has minimal exposure and experience with children and adults with special needs, resulting in unawareness, avoidance, and a general misunderstanding of this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".