Bibliographic record
Abstract
This is our second volume devoted to the studyo ft he concepts of 'humour' and 'cruelty',a nd we have alreadyc ommitted to writing at hird.Inevitably, we have had ample opportunity to brood over the notion that the twoo fu s, as authors, have set ourselves up for avery cruel joke.Perhapsnoone will be remotelyinterested in this colossal undertaking which has taken years of our livesand adecent portiono fo ur already-fragile sanity.I magine two expectantm iddle-aged academics pouring over at ext form onths on end, hoping that their book will make an impact,o nlyt oh avei tc ollect dust in some library,o rw orse,i nside awarehouse.Is it all for nothing?J ust some frantic wriggling of two occasionallys entient hominids soon to be dead and forgotten?Acruel joke, indeed.Cruel humour makes light of both our suffering and meaning in life.Given that the former is inescapable and the latter elusive,humour mayserveasanimportant copingstrategy in helping us navigate the adversity as well as the absurdity inherent in theh umanc ondition.But then submitting meeklyt os uffering or givingu po ne xistential meaning is by no means an obvious and flawless recipe for ag ood life.Thus, we have come to the conclusion that hope and faith are, as it happens,the onlycredible ways forward.Promisingly, in this respect,manyvaluable souls have alreadydemonstrated their faith in our project.Therefore, let us make proper use of these brief acknowledgments and displayi np rint our most sincereg ratitude and appreciation.Lydia Amir should be recognised as the museo fo ur threev olumes.S he has shown exceptional generosity and fortitude throughout the sometimes-bumpy writing process.Weare also deeplyindebtedtoChristoph Schirmer,aphilosopher himselfand the Senior Acquisitions Editor for Philosophy at De Gruyter's.Not only has he been extremelypatient and exemplarilyprofessional, but he also made our endeavour an even crueller joke by transforming theoriginal project from asingle monograph into three distinct volumes.Inaddition,wewant to extend our thanks to RayS nider,w ho so ably assisted us with the proofreading and editing of the tome.We must also mention KenD orter,P rofessor Emeritus at the University of Guelph in Canada, whog aveu sm uchv aluable feedback, especiallyw ith regard to thef eminist and ethical studiesc onducted by the late Jean Harvey.Very special thanks go to the noted French cartoonist Thibaut Soulcié, who provided us with an English-language version of ac lever creation of his.As the reader will hopefullyb ea ble to gather at al ater point in this book, his depiction of ab ittersweet clown confronted with ac ruellyi mpossible humorous task is the iconic rendition of several important themesr unning throughout this volume.Lastly, we must express our gratitude in the most emphatic manner to our spouses
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.466 | 0.308 |
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".