Plantemic: A Philosophical Inquiry and Storytelling Project about Human-Plant Co-Existence during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic in 2020 has created a particular shared human condition. Never in recent history have we spent so much time social distancing at home, living and working in relative isolation. For many of us isolating at home, our beloved and loyal houseplants became our sole companions; our existence interlaced with our houseplants, who are also living, breathing, possibly working, grieving, and connecting in times like this. \nThis project undertakes a philosophical and creative inquiry inspired by human-plant co-existence during this time of uncertainty and crisis. Specifically, it asks how might we understand human-plant relationships during the COVID-19 pandemic; and in particular, what might this indicate about our sense of our existence? Answering this question involved designing research methods that collected qualitative data on human-plant relationship during the pandemic and the making of Plantemic, an animated web series that tells the stories of the pandemic from fictionalized plant characters’ perspective. \nIn this journey, I have navigated discourses in design, foresight, media studies, psychology and philosophy. I explored how futurist storytelling should be re-approached in a time when we are experiencing thorough disruption and existential angst. To this end, I sought inspiration from love letters and break-up letters that plants’ human friends submitted as part of this project in order to uncover a story about connection, intimacy and acceptance between human and plants during the pandemic. Ultimately, I hope to present to you, through this report and the animated web series, that to make sense of our existence and to reorient ourselves, we must accept and cherish existence as not something solely puts humans at the centre, but rather existence as the transcorporeal, interconnected, and co-created existence with plants and by extension nature.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".