Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The thesis of this short note is that post-intervention probabilities can be considered to be certain conditional probabilities. Acyclic causal modelsLet us consider an acyclic causal model M of the sort that is central to causal modeling (Spirtes et al. 1993/2000, Pearl 2000/2009, Halpern 2016, Hitchcock 2018).Readers familiar with them can skip this section.M = S, F is a causal model if, and only if, S is a signature and F = {F 1 , . . ., F n } represents a set of n structural equations, for a finite natural number n. S = U, V, R is a signature if, and only if, U is a finite set of exogenous variables, V = {V 1 , . . ., V n } is a set of n endogenous variables that is disjoint from U, and R : U V R assigns to each exogenous or endogenous variable X in U V its range (not co-domain) R (X) R. F = {F 1 , . . ., F n } represents a set of n structural equations if, and only if, for each natural number i, 1 i n: F i is a function from the Cartesian product W i = XUV\{V i } R (X) of the ranges of all exogenous and endogenous variables other than V i into the range R (V i ) of the endogenous variable V i .The set of possible worlds of the causal model M is defined as the Cartesian product W = XUV R (X) of the ranges of all exogenous and endogenous variables.A causal model M is acyclic if, and only if, it is not the case that there are m endogenous variables V i1 , . . ., V im in V, for some natural number m, 2 m n, such that the value of F i(j+1) depends on R V i j for j = 1, . . ., m -1, and the value of F i1 depends on R (V im ).Importantly, dependence is just ordinary functional dependence: F i depends on R V j if, and only if, there are arguments w i and w i in the domain W i = XUV\{V i } R (X) of F i that differ only in the value from R V j such that their values under F i differ, F i w i F i w i .
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
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 it