Evaluation of the Effects of Nanofluid on the Lubricity of Oil-based Mud
Bibliographic record
Abstract
Fiji Hindi (FH), which developed as a result of plantation contact during the indenture period (1870–1920), is identified by about 37.5% of Fiji’s total popu- lation and by a considerable diasporic Indo-Fijian population as their mother tongue (Fiji Bureau of Statistics, 2007; Mangubhai & Mugler, 2006, p. 97). Although this speech community perceives FH as its heritage language, an iden- tifiable generic term has never been adopted to describe this or any other herit- age language in Fiji.1 FH is the language of girmitya descendants – indentured laborers brought to Fiji by the British to work on sugar and cotton plantations from 1870 to 1920.2 \nThe absence of a label for heritage languages is not unique to Fiji. The defi- nition changes from place to place, differing with community power, language proficiency, and individual heritage (see Fishman, 2001). Hornberger and Wang (2008) define heritage language users as individuals with familial or ancestral ties to a language other than English who exercise their agency in classifying them- selves as users of a heritage language. They state that this determines how these individuals negotiate their identity with other dominant cultures and standard languages they come into contact with (Hornberger & Wang, 2008, p. 6). The critical aspect of this definition requires self-selective membership of the heritage language community.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".