Fetal Alcohol Spectrum Disorder
Bibliographic record
Abstract
Background and Objective The manner in which language is used reflects how people in a society view one another. Historically, individuals with disabilities have experienced discrimination through the use of stereotypic or demeaning language. Individuals with Fetal Alcohol Spectrum Disorder (FASD) may be particularly susceptible to these negative impacts, particularly given the stigma associated with the disability. We discuss how individuals with disabilities may be affected by our use of language. Materials and Methods Current definitions of FASD from Canadian provincial/territorial, national, and international governments and organizations were collated. Recent academic definitions found in the peer-reviewed literature were also reviewed. All definitions were independently coded by the two authors to identify definitions which were based upon current and emerging evidence and which included factual information about FASD. A standard definition of FASD was developed through an iterative process, including expert consultation and feedback from the larger FASD community. Results We propose an evidence-based, lay-language standard definition of FASD to be used in a Canadian context, intended to reflect the range of strengths and challenges of individuals with FASD as well as the whole-body implications of the disability. Conclusion Our standard definition of FASD provides an opportunity to ensure consistency in language, increase awareness of FASD, promote dignity, and reduce stigma upon people with FASD and their families. We encourage governments, policy makers, service providers, and researchers to adopt the authors standard defi-nition of FASD, with the goal of increasing awareness of FASD, reducing stigma, and improving communication and consistent messaging about the disability.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".