The Use of Photo-Essay to Report Advances in Applied Science and Health
Bibliographic record
Abstract
Background: In the applied health and science disciples there is an expectation that project work is reported through a publication. The conventional papers written to do this follow a structure that includes sections providing background, methods, results and a discussion or conclusion, supported by figures and tables. Sometimes photographs are included, and with more on-line publications the opportunities have increased for these to be available in full color. Borrowing from the field of photojournalism photo-essays are now a publication option where a series of images are used to tell the story; these are often related to health and well-being.Aims: To summarize the methodology used to effectively combine a series of images with a brief text, and short reference list to create a visually engaging and informative short report.Guidelines: Images are taken throughout the project with consent obtained from those whose images will be recognisable. Creative licence is used to compile representative images into a sequence that conveys the background, method, results and outcome(s) of the project. Images need to be of high resolution; editing for light, colour and contrast, and cropping is allowed to increase their clarity and relevance. The ethics of photojournalism apply making inappropriate manipulation of images or erroneous captions unacceptable.Conclusions: Photo-essays are a novel and informative way to report on an applied health, social or scientific topic. The format is an excellent one to use for a brief report, or to prepare a research presentation for a scientific meeting.Keywords: Photograph, Photojournalism, Photo-manipulation.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.069 | 0.028 |
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".