Doing History: Introducing historical research methods
Bibliographic record
Abstract
[Extract] Historical research is an area of research that remains elusive to many mainstream nursing researchers. Further complicating the matter is there a relative lack of literature explaining the how and why of this methodology. Put simply, historical research analyses a past event, concept, place or person in an attempt to address a knowledge gap in the literature. Historical research is guided by a research question, aim and objectives like all other forms of the inquiry. Historical researchers believe that there is an interconnectedness of variables such as context, time and place and attempt to better understand how such interconnectedness effected or signified past change. Historical narratives, the outcome of the research process, explicate these connections to the reader to increase awareness about why the past event or phenomenon being studied may have occurred or transpired. The incorporation of temporal, spatial and contextual information moves the research beyond being a simple description to a sophisticated understanding of the research topic. While historical research is primarily qualitative in nature, quantitative analysis approaches are becoming more common with technological advances including of course the advent of information technology. Examples include Erin Spinney’s , a Canadian historian, amazing work whereby she used spreadsheets to analyse the demographical data and wage rates amongst eighteenth and early nineteenth century British naval and military nurses but also more technologically driven initiatives such as using geographical information systems (GIS) to inform the historical researcher on the spatial considerations of their topic. Furthermore, the emergence of digital technologies and the progressive digitisation of historical sources means that researchers are increasingly turning to artificial intelligence to assist with quantifying their analysis. Finally, regardless of the type of inquiry, research topic or period being studied, historical research generally follows a set of methodological norms that go far beyond the use of footnotes and these will be addressed throughout this webinar.
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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 | 0.068 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".