What Does a Microscopist Look Like? An Exploration of Vintage Ads and Brochures in Microscopy and Microanalysis
Bibliographic record
Abstract
When you picture a scientist, what do you see?This question has been a starting point for discussions around diversity, equity and inclusivity in STEM.When searching for "microscopist" at online suppliers of digital stock images such as Getty Images [https:// www.gettyimages.ca/]the search dominantly returns wood cuts and photographs of microscopists from the 17 th to 19 th century.Searching for "microscope" returns more current imagery of people operating microscopy equipment with some diversity of genders and races while a simple google image search seems to yield a more diverse range of images comparatively (Fig. 1).In this poster -expanded and supported by an online image gallery -I want to share a growing collection of vintage ads, and brochures that contain pictures of microscopists through the last decades (Fig. 2).The aim is to collate material that could aid conversations about historical and modern depictions of microscopists and microanalysts and how they accurately or inaccurately reflect demographics, especially the visibility of women and underrepresented minorities in this profession at the time.Poster attendants will be encouraged to share their own thoughts and observations on the presented material and how our community can add dimensionality to the image of microscopist and microanalyst.Fig. 1.Google image search results for "microscopist" in February 2023.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".