Limits...
Phylomemetic patterns in science evolution--the rise and fall of scientific fields.

Chavalarias D, Cointet JP - PLoS ONE (2013)

Bottom Line: We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields.We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns.Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.

View Article: PubMed Central - PubMed

Affiliation: Complex Systems Institute of Paris Ile-de-France, Paris, France. david.chavalarias@ehess.fr

ABSTRACT
We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields. We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns. Some structural properties of the scientific fields - in particular their density -, which are defined independently of the phylomemy reconstruction, are clearly correlated with their status and their fate in the phylomemy (like their age or their short term survival). Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.

Show MeSH

Related in: MedlinePlus

Evolution of the density in the vicinity of special events.A. Variation of density in the vicinity of a branching node. B. Variation of density in the vicinity of a merging node. Error bars represent the 95% confidence interval.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3569444&req=5

pone-0054847-g008: Evolution of the density in the vicinity of special events.A. Variation of density in the vicinity of a branching node. B. Variation of density in the vicinity of a merging node. Error bars represent the 95% confidence interval.

Mentions: To gain a better understanding of what shapes the structure of the phylomemy and therefore the diversity of dynamic patterns encountered in science, we studied the variations in density around special events at the meso-level of clusters. After the removal of the branches covering less than 3 periods, which rendered the data noisy, we found a remarkable U-shaped curve indicating that, on average, the density reaches a local minimum at these critical points (cf.Fig. 8). The density significantly decreases before a special event takes place and increases during the following periods.


Phylomemetic patterns in science evolution--the rise and fall of scientific fields.

Chavalarias D, Cointet JP - PLoS ONE (2013)

Evolution of the density in the vicinity of special events.A. Variation of density in the vicinity of a branching node. B. Variation of density in the vicinity of a merging node. Error bars represent the 95% confidence interval.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3569444&req=5

pone-0054847-g008: Evolution of the density in the vicinity of special events.A. Variation of density in the vicinity of a branching node. B. Variation of density in the vicinity of a merging node. Error bars represent the 95% confidence interval.
Mentions: To gain a better understanding of what shapes the structure of the phylomemy and therefore the diversity of dynamic patterns encountered in science, we studied the variations in density around special events at the meso-level of clusters. After the removal of the branches covering less than 3 periods, which rendered the data noisy, we found a remarkable U-shaped curve indicating that, on average, the density reaches a local minimum at these critical points (cf.Fig. 8). The density significantly decreases before a special event takes place and increases during the following periods.

Bottom Line: We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields.We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns.Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.

View Article: PubMed Central - PubMed

Affiliation: Complex Systems Institute of Paris Ile-de-France, Paris, France. david.chavalarias@ehess.fr

ABSTRACT
We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields. We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns. Some structural properties of the scientific fields - in particular their density -, which are defined independently of the phylomemy reconstruction, are clearly correlated with their status and their fate in the phylomemy (like their age or their short term survival). Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.

Show MeSH
Related in: MedlinePlus