Bibliographic record
Abstract
The purpose of the current study was to evidence how ‘mixing’ is interpreted by researchers and to draw interpretations that would continue to map how mixed research contributes to the advancement of scientific inquiry. The data source consisted of peer-reviewed abstracts of mixed research papers that were accepted for presentation at the 2012 annual meeting of AERA in Vancouver, British Columbia. A parallel mixed analysis was implemented in two phases. Phase 1, descriptive data were compiled (frequencies percentages) detailing the prevalence of mixed research topics in the abstracts. Phase 2, a content analysis involving a text analysis was implemented, and the results were analyzed utilizing within-case and cross-case analyses. Specifically, each abstract (i.e., case) was read and the abstract’s content and the author-generated descriptors were used in tandem to generate a context that specified the focus of each topic (i.e., contextual descriptors). Additionally, to ascertain the methodological focus of each abstract, the abstracts were categorized in accordance to the three components comprising Teddlie and Tashakkori’s (2010) ‘Emerging ‘Map’ of Mixed Methods Research. To continue further this line of documentation, each of the seven articles in this special issue was mapped to one of three components comprising the map. Results indicated a balance of educational topics categorized across the three components. Implications are discussed in the context of responding to the question ‘Is Mixed Research Science?’
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 | 0.011 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.443 | 0.098 |
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".