Where Research Begins: Choosing a Research Project That Matters to You (and the World)
Bibliographic record
Abstract
Original research is essential in academic studies, especially at the graduate level.While students can take coursework specifically on research methodologies, additional resources, such as books on research writing, can be beneficial to students' work.However, most of these texts focus on how to do the research, not on helping students decide what to research.Mullaney and Rea's 2022 publication, Where Research Begins: Choosing a Research Project that Matters to You (and the World), fills this gap, and should be an essential component of every student's journey through academic research.Written as both a guidebook and a workbook, this small-but-mighty resource shows students how they can start their path for research.The book aims to hone students' ability to ask questions that lead to uncovering an underlying research problem from which a project can be formed.The book also serves as a workbook, with exercises and prompts for the reader to try this now, and to apply the learning in the chapters with their own research interests to ultimately generate a working draft of an actual research project.As a great complement to the reading and writing activities, each section of the book also includes commonly made mistakes that the authors highlight, so that readers/writers can avoid them in their own work.The authors also suggest finding a sounding board-a professor, a mentor, an advisor, or a colleague who acts as an outside eye, offering a different perspective or pointing out unconscious thinking or assumptions.
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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 | 0.078 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.031 |
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".