Incorporating Immigrant Perspectives Into Organizational Research and Practice: Implementing Inclusive Discussions
Bibliographic record
Abstract
Anthropologists have "been in business" with for-profit and not-for-profit organizations (NPOs) for most of the 20th century, and their role as consultants for such corporations, research firms, and local organizations has continued to grow since this time (Jordan 2013). When they invest in community-based research through these partnerships, NPOs often hope to acquire meaningful and relevant evidence about practices in their communities. Yet, NPOs are unable to realize many of their potential collaborations with academics due to their dependency on elaborate and increasingly competitive funding frameworks constructed by granting bodies (INTRAC 2012). Furthermore, Morris and Luque (2011) have argued that community-based organizations and coalitions have limited input from the populations they hope to represent. Consequently, the representation and inclusion of diverse populations throughout the research process continues to be a struggle. This includes participation in data collection, project development, creation of evaluation measures, and the negotiation of program and/or policy development. Despite these limitations, participatory-action research is shown to provide long-term partnerships between both academics and their collaborators (INTRAC 2012).
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 | 0.229 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.041 | 0.049 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.007 | 0.064 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier 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".