orm_autodim: Automatic dimension extraction and risk cross-matrix

In orisma: Occupational Risk Integrated Systematic Mapping and Analysis

Automatic dimension extraction and risk cross-matrix

Description

orm_autodim() automatically discovers the most relevant contextual dimensions of a corpus using two complementary modes:

Mode 1: Dictionary blocks (default, method = "blocks") Uses the normative blocks of the ORISMA dictionary (A-Safety, B-Hygiene, C-Ergonomics, D-Psychosociology, E-Biological, F-Emerging) as dimensions. Computes a block x block co-occurrence matrix showing how many studies address combinations of risk blocks simultaneously. Works for any corpus without any configuration.

Mode 2: Free text (method = "text") Extracts discriminant terms from abstracts using TF-IDF-like filtering. Useful for discovering domain-specific dimensions not covered by the dictionary (e.g. specific materials, sectors, tasks).

Usage

orm_autodim(
  mx,
  method = "blocks",
  text_col = "abstract",
  n_dims = 12L,
  min_freq = 3L,
  max_doc_pct = 0.35,
  min_cooccur = 0.5,
  fuzzy_sim = 0.85,
  stopwords = NULL,
  lang = getOption("orisma.lang", "en"),
  verbose = getOption("orisma.verbose", TRUE)
)

Arguments

mx	An orisma_matrix object from orm_extract().
method	Character. "blocks" (default) or "text".
text_col	Character. Text field for method = "text". Default "abstract".
n_dims	Integer. Max dimensions for method = "text". Default 12.
min_freq	Integer. Min document frequency for method = "text". Default 3.
max_doc_pct	Numeric (0-1). Max document proportion for method = "text". Terms above this are too generic. Default 0.35.
min_cooccur	Numeric (0-1). Min co-occurrence with a risk. Default 0.5.
fuzzy_sim	Numeric (0-1). Fuzzy grouping threshold. Default 0.85.
stopwords	Character vector. Extra stopwords for method = "text".
lang	Character. "en" or "es".
verbose	Logical.

Value

A list (class orisma_dims) ready for orm_dim_matrix().

See Also

orm_dim_matrix()

orisma documentation built on May 19, 2026, 1:07 a.m.