ANALYZING IN-TEXT CITATIONS IN UNDERGRADUATE PROJECTS: A CASE STUDY FROM KABALE UNIVERSITY
Bibliographic record
Abstract
In-text citation is a critical facet of scholarly research, playing a pivotal role in the construction of a comprehensive and robust literature review. This practice entails acknowledging and referencing all sources cited within the body of a research work, reflecting an ethical and scholarly commitment to recognizing the contributions of prior works. In this context, the distinction between citation and referencing is elucidated, with the former focusing on acknowledging sources within the text, while the latter pertains to the systematic attribution of ideas, theories, quotes, facts, and other supporting evidence in academic assignments, papers, seminars, dissertations, and theses. Scholarly communication relies on various referencing styles, and this paper explores the significance and nuances of two prevalent citation systems: Harvard Referencing and the American Psychological Association (APA) style. While multiple referencing styles exist globally, Harvard Referencing and APA stand out as popular choices in academic writing, depending on the discipline. The selection of a particular citation style is often driven by the conventions of the academic community and the subject matter of the research. Citation styles serve as standardized frameworks for attributing and acknowledging sources used in research papers or assignments. Notably, the choice of a citation style is often mandated by the specific program or degree being pursued, reflecting the need for uniformity and consistency within academic writing. This paper delves into the intricacies of the APA citation style, shedding light on its applicability in Social Sciences, education, and related fields. Furthermore, it introduces alternative style options such as the Modern Language Association (MLA), Council of Science Editors (CSE), and Vancouver, offering a comprehensive overview of the diverse referencing landscape available to researchers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".