Flattening the Facebook Curve: Exploring Intersections of Critical Mathematics Education With the Real, the Surreal, and the Virtual During a Global Pandemic
Bibliographic record
Abstract
In March 2020, near the onset of the COVID-19 related lockdowns, quarantine, and isolation measures being taken worldwide, we noticed an increasing number of graphs, diagrams, images, and mathematical models relating to the pandemic posted on our Facebook walls. For the purposes of this paper, we selected a number of these Facebook posts to discuss and analyze, through the lens of questions based in critical mathematics education research. Our analyses draw attention to public discourse(s) around mathematics, as well as how numbers, graphs, diagrams, and images are used on Facebook. In our analyses, we first identify the mathematics topic/concept being depicted through the image and, second, how that Facebook post might serve as an artefact of critical mathematics education. In doing so, we challenge the usual separation of mathematics classrooms from the real world and highlight how, in this time of pandemic, life is less real than it is surreal; it is less real than it is virtual.
 Keywords: mathematical modelling; real-world problems; images, critical mathematics education; mathematics and social media; virtual reality; Facebook; mathematics in society; mathematics teaching; mathematics teacher education
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".